E-learning expectations for Korea’s lifelong learning in
the “ubiquitous society”
I. Introduction
1. Need and purpose of research
Internet and web technology has become one of the most effective teaching and
learning tools, and e-learning is being increasingly perceived as a critical part of the
learning environment. At the same time, education and training are now regarded as
lifelong processes. As a result, e-learning that allows access to learning opportunities
regardless of time and space is being promoted as a learning model for adults pursuing
lifelong education.
Lifelong learning has been rapidly developing in South Korea since 2000 with
government support. It has increased social integration and national competitiveness
through the creation of a lifelong learning society, which enables everyone to learn
anywhere and at anytime through centers for lifelong learning in universities and
provinces. E-learning has been widely used over the entire span of lifelong learning,
from school education to career training and re-employment services, since six
governmental bodies started cooperating following the passage of the E-learning
Industry Promotion Act by the Ministry of Education, Science and Technology in 2009.
At present, information communication technology (ICT) is one of the platforms used to
implement lifelong learning, and e-learning is regarded as a strategy to achieve lifelong
learning because it reduces the distance between instructors and students, as well as the
distance among students. Furthermore, it has been proven effective in improving
educational quality through standardization and open education.
E-learning has become an integral part of lifelong learning, and its program
development and application is being accepted without resistance. E-learning differs
from conventional learning in three critical ways: it can take place anywhere a computer
can be connected to the Internet; content from e-learning sites is consistent and qualityassured, and; content is not fixed but adapted to each learners’ level and style. Even the
process of finding information and forming knowledge through e-learning exhibits
individuality and activeness (Jung Min Seung et al, 2010).
In the ubiquitous society with the advent of these e-learning characteristics and the
recent rapid development of technologies, such as mobile technology and ubiquitous
computing, the idea of lifelong learning society has been realized. Anybody can engage in
learning anywhere at any time. The word “ubiquitous” means being and existing
everywhere at the same time, and ubiquitous computing is a technology that provides
needed information and services to users on the spot: various computational devices are
pervasive in people, objects and environments to connect anywhere at any time. Unlike
existing e-learning, the ubiquitous environment enables learning to take place beyond
the limits of cyberspace or physical space with the help of ubiquitous computing
technologies. Moreover, it provides curriculum that is tailored and adapted to learners,
which can better satisfy lifelong learners. Nonetheless, e-learning in the ubiquitous
society is not made from nothing.
One needs to first understand the meaning and the features of ubiquitous computing
and grasp the demands of e-learning in the ubiquitous society, such as the conditions
and preferences of learners, and then design and develop programs accordingly to
operate effective lifelong learning programs..
There is a need to design and develop e-learning programs that can enhance the
benefits of the ubiquitous society by moving beyond uniform e-learning methods that
merely upload existing content onto the web. To this end, an in-depth study should be
conducted on members of lifelong learning institutes to find out how they envision elearning for lifelong learning in the ubiquitous society. For this study, a needs analysis
for e-learning was conducted on learners at lifelong learning institutes (open
universities, cyber universities and centers for lifelong learning), and education
professionals, including teachers and lifelong educators who design lifelong learning
programs. Finally, strategies for the effective use of e-learning for lifelong learning in
ubiquitous society are suggested.
2. Research Process
1) Concept define of ubiquitous society and lifelong learning
The concepts of ubiquity and a ubiquitous learning environment are defined and the
features of u-learning are examined. In addition, a preliminary study was conducted on
the status of lifelong learning information systems in Korea, and to investigate and
analyze supply and demand for lifelong learning programs.
2) Fact analysis of e-learning participation in lifelong learning
Through the structured questionnaires, program profiles and participation rates by
lifelong learning participants’ type were analyzed. Learners’ e-learning experience, elearning methods and environments, and their learning tools were examined and
analyzed according to gender, age, occupation, wage, and learning style.
3). E-learning needs analysis in a ubiquitous environment
A needs analysis for u-learning participation and activation was conducted on
learners, lifelong educators and teachers. On the basis of this, recommendations for
improving ubiquitous learning environments for lifelong learning in Korea were made.
II. Theoretical background
1. Ubiquitous environment
The concept of the ubiquitous environment is often heard and read in the media. It
emphasizes access to any service via any communication device through any
communication networks anywhere and at any time. The concept is pervasive and
includes words such as u-learning, u-government, u-city, u-health, and u-shopping.
Lifelong learning is no exception and the ubiquitous environment has been much
discussed as the most optimal method to realize lifelong learning (Jae Bun Lee et al,
2006; Dong Man Lee, Sang Hee Lee, 2009).
1) Ubiquitous environment and u-learning
The word “ubiquitous” derives from the Latin word “ubique” and means “being and
existing everywhere at the same time.” It is used here to indicate an environment where
one can use various information communication services while getting access regardless
of time and space through a network. Ubiquitous networking technology integrates
various objects with computer and information communication technologies, allowing
users to communicate with them anywhere and at any time. As a result, this is
understood as the concept expression of an information communication technology, and
various terms such as “ubiquitous computing,” “ubiquitous networking,” “ubiquitous IT”,
and “ubiquitous society” – which have a more comprehensive meaning – are used with
no clear demarcation (Jae Yun Kim, Ki Duk Kwon, Jin Hwa Lim, 2004). The
characteristics of this ubiquitous environment are omnipresence, intelligence and
constancy (Son Mi, 2007).
”Omnipresence” is the interconnection between computational devices and various
other objects and places with an emphasis on communication between people and
objects or among the objects. One way to emphasize this feature is to embed
computational functions in the objects or to enhance the portability or mobility of the
objects. One example of this is to turn off a light or control the temperature of a house
via a mobile phone while outside the house.
”Intelligence” occurs when computational devices actively perceiving and responding
to environments and situations. This is the greatest difference between conventional
computing and ubiquitous computing: computers perceiving the environment, providing
the needed environment for users, and taking necessary actions while making its own
judgement. For example, the computer inside a refrigerator inspects food inside and
restocks what has run out, such as milk and vegetables. Providing needed learning
materials after understanding learners’ adaptive learning can become another example
of intelligence.
“Constancy” denotes a switch from the grid computing environment, which is
dependent on time and space, to the state of being able to access a network anywhere
(Electronic Newspaper, 2005). With the introduction of smart phones and smart tablets,
many people already experience the constancy of ubiquitous environments .
With these three characteristics, the ubiquitous environment brings about many
changes in educational environments. The word “u-learning,” which means learning in
the ubiquitous age, has been coined and the government has been making various efforts
to realize this. U-learning enables and motivates learners to learn anywhere and at any
time while tailoring curriculum to them to study on their own. In short, it is learnercentered.
The u-learning environment can utilize a learner’s living environment as learning
resource and is a friendly environment where acquired knowledge pervades in real life.
On the premises of an educational environment where learners can learn anywhere and
at any time with any device, u-learning offers educational courses that are creative and
learner-centered. Hence, with the use of distance education and outdoor classrooms,
which are beyond the boundaries of traditional classrooms, u-learning enables optimal
learning that is tailored to learners’ age and styles with no restrictions in time and space.
Learners use the network inside and outside the classroom while absorbing lecture
content vividly in dialog format, and distance learners use ubiquitous technologies to
take and review lectures.
U-learning has several features that are different from e-learning (Guen Sang Park et al,
2007; Dong Man Lee, Sang Hui Lee, 2009). Firstly, while e-learning uses cable Internet
and the web technology, u-learning uses wireless Internet, augmented reality and virtual
web-technology. Secondly, e-learning is based on the network between computers
whereas u-learning is based on the network between wireless devices, acquiring the
information of learners’ locations and needed information on the spot through sensors,
chips and tags installed in the devices. It is a form of learning that acquires information
from both learners and objects. Kwak (2009) indentifies six new impacts of u-learning.
Firstly, there will be less dependence on educational venues and devices. With the
expansion and improvement of information communication networks, and various
media-learning supports, learning is made possible by connecting to a network is
possible through devices other than a computer, such as TV, mobile phone, and e-books
devices.
Secondly, it is possible to shift from the pull model to the push model. The pull model,
in which learners had to access the learning environment intentionally, was less
effective for those with weak motivation or those in disruptive learning environments.
On the other hand, in an environment where access to learning is possible anywhere and
at any time, it is possible to have tailored education (the push model) that
accommodates educational needs for learners on the spot anywhere.
Thirdly, it is possible to observe learners through intelligent devices and offer
tailored information to their levels. This can be used to arouse learning interests and
induce learning.
Fourthly, individualized instruction in general is easier and with the development of
individualized education, the expansion of distance education has become less
problematic. Group education enables the operation of various educational programs
according to learners’ interests and their academic achievements, and provides learning
methods according to the learners’ levels using intelligent programs.
Fifthly, education will not end as a one-off but will develop into various methods in
different places. In particular, it can provide education in an integrated form of various
resources, going beyond the limitations of physical space called schools.
Sixthly, as permanent and various assessment systems are made possible, the
continuous improvement of educational methods can be carried out along with a
feedback system that gauges learners’ aptitudes. Collaborative learning can take place
actively and with the improvement of accessibility, expert knowledge can be widely used
in education.
2) Ubiquitous environment and lifelong learning
Lifelong learning is education from the cradle to the grave, covering formal, informal
and non-formal education. Hence, rather than being another field of education, it is a
transformation in perspective away from the concept of education being something
restricted to schools (Dodds, 2003). In the past, we used to believe that education and
learning were confined to school, and did not recognize the value of non-formal
education. However, as learning breaks the boundaries of schools and the importance of
lifetime learning has come to the fore, the value of non-formal education, which focuses
on day-to-day life and livelihood outside of formal education, is increasing (Jae Bun Lee
et al, 2006).
Learning is not confined to school. People also experience and acquire new
knowledge and skills from television, reading books, surfing the net or conversations
(Albeit & Dausien, 2002). Combining the characteristics of lifelong learning, which
happens in various forms in a lifetime, and new innovations of Internet communication
technology, lifelong learning with the use of ICT has been in the limelight. The lifelong
learning and e-learning with the use of ICT have been growing rapidly since 2002 with
the help from the government. The Korean government judged lifelong learning which
happens anywhere and at anytime.
As mentioned in the previous chapter, the omnipresence, constancy, and intelligence
of a ubiquitous environment, provide for not only the expansion and automation of a
learning environment but are also sources of diverse knowledge and a broad array of
learning choices. This, in turn, leads to the diversification of education, transcending the
fixed content of educational curricula and allowing for the diversification of learning
styles and models, as well as flexibility in learning management through information
communication technology. Through this, a learner-centered lifelong learning
environment will be realized and the quality of lifelong learning will be high with the
emergence of new learning communities (Jae Bun Lee et al, 2006).
2. Lifelong learning trends in the ubiquitous society
Ubiquitous computing does not end with simply improving the computer environment .
It is expected to change the topography and culture of learning. Countries with advanced
computerization advocate for the establishment of ubiquitous environments at the
municipal and central government levels. With the expansion of educational services via
mobile devices, the market for u-learning education has grown.
The main changes for lifelong learning systems, and teaching and learning in the
ubiquitous society are as follows.
Firstly, the openness and development of lifelong learning systems and policies has
been promoted as flexible, unlike the institutionalized system with only one route:
individualized learning tailored to learning conditions, hours, space and schedule has
become possible. Breaking away from the paradigm of delivering the educational
contents, support for “autonomous learning” that can develop learners’ aptitudes
according to their contexts has been emphasized (Lim, So & Tan: 2010). As a result,
there has been increasing interest in systemic devices, as well as teaching and learning
models that enables flexible and individualized learning.
Secondly, with the development of ubiquitous technologies, great emphasis has been
placed on critical judgment of information technology along with skills for making good
use of online learning technologies. Not only has the importance of tool literacy – such as
computer literacy, network literacy and technology literacy – been emphasized but also
the ability to produce, evaluate and interpret information that has been delivered via
various devices. Furthermore, critical research in and the importance of learning
information technology at the social and cultural level have been also stressed (Kerka,
2000).
With regard to the digital divide, efforts to promote the educational use of and access
to technologies by different groups and economic backgrounds are being made.
Examples include the U.S. non-profit educational organization called Seniornet, which
contributes to the use and supply of online learning technologies for the elderly, and the
Ansan support center for migrant workers in Korea, which runs educational programs to
promote the use of media for migrant workers.
Thirdly, interactive learning and learning via a network are active. Independent and
isolated distance learning in the industrial age has been replaced by interactive and
collaborative learning, which is based on different mobile media, social networking
services and information communication infrastructure. In the same vein, there have
been efforts, made to promote human interactive learning in the e-learning system
development for lifelong learning. “Human e-learning” and “peer to peer e-learning” at
Japan’s Yashimagakuen University are the main examples of this effort (Yamamoto,
Takumi, & Matsuo, 2009).
Fourthly, lifelong learning pursues diversification and holism.
As the routes for
informal and non-formal education increase, knowledge, attitudes and skills acquired
from them is increasingly recognized. The research, development and application of
technology that can supervise and accumulate daily learning is expanding. As part of this
effort, tools such as ”lifelong learning organisers (LLOs)” that support an individual’s
learning experience, educational resources, records, organization and publication , are
being developed. A few quintessential examples that show the trend of lifelong learning
in the ubiquitous environment are as follows.
○ Open Educational Resources (OER)
Open Educational Resources means free digital resources that are open to the public
for the research and learning of educators, students and autodidacts. OER includes
learning content, content development and use, needed software for distribution and
necessary measures for copyrights. The number of OER projects and activists, and the
amount of educational resources, are rapidly increasing. From 2006 to date, 3,000 kinds
of open courseware have been offered from approximately 300 universities around the
globe (OECD, 2007). When looking at the background of sharing resources freely, first
and foremost it is easier to produce content as user-friendly IT infrastructure, software
and hardware that is accessible at a low cost. Sharing of the educational contents also
lowers the production cost. Copyright for free content sharing and use has not been
solved legally and there is high social reliance on content sharing. In the eyes of the
government, OER offers opportunities for higher education to those who have not
received the benefits and has become an effective tool for advertising private and public
lifelong learning programs. It can be used to narrow the gap between informal education
and formal education. Furthermore, for educational institutes knowledge sharing can
help them commit to their true traditions and purpose while improving the quality of
their contents and lowering the costs. Since OER is effective in itself in advertising an
institute it can stimulate the development of new educational resources and internal
innovations, contributing towards the promotion of competitiveness. Hence, the scope of
applying open license to secure sharing and using resources whose copyrights have been
protected in cyberspace has been expanding without any risk of copyright infringement
(OECD, 2007).
When looking at policy implications of OER, copyright regulation, interoperability and
establishing a knowledge base of OER activities have become the educational issues at a
national level. The expansion of OER also bridges the gap between the formal and
informal education. Hence, the establishment of lifelong learning programs, the
diversification of educational resources and supply channels with the use of OER have
become important tasks (Hylén, 2008).
Active application of OER also breaks the boundaries between educational content,
users and developers. This occurs not only in organizations but also in individuals as it
opens opportunities to take part in knowledge creation and sharing independently.
Hence, the quality control of OER content and the easing on copyright policies remain as
major tasks.
○Seniornet in the U.S. (http://www.seniornet.org/ )
With the rapid advancement of computerization, even senior citizens use the computer
for various purposes. Seniornet in the U.S is a non-profit organization that teaches the
elderly how to use computers and the Internet in order to improve the quality of their
lives and allow them to share wisdom and knowledge.
At first, Seniornet focused on computer education for the elderly but as it developed
over the years, it now offers educational programs that satisfy the various educational
needs of elderly people. Its members can freely use any Seniornet center throughout U.S.
to receive basic computer education and also access advanced programs, such as
computer statistics, graphics, personal financial management, tax filing,, etc. Senior
citizens first learn how to use computer and then learn how to use it to learn what’s
needed in real life. Seniornet provides educational services throughout the United States
through regional learning centers and an online learning center, offering about 300
courses.
The first level offered by Seniornet at its online learning center is ”learn more about
your computer,” which teaches how to navigate a computer and Microsoft word and
excel programs. The second level is ”get connected,“ which teaches how to get connected
to the Internet and social networking tools, such as email and Facebook, to connect with
families and friends. The third level is ”explore the World,” which allows learners to use
web-based educational programs recommended by the lecturers and staff.
When looking at the implications of Seniornet on lifelong learning in the ubiquitous
society, it does not end at teaching how to use various information communication
technologies, but also introduces and connects seniors to educational activities offered
on the web. It has designed educational courses suitable for the physical, psychological
and social characteristics of elderly learners through online and offline learning centers,
creating a learner-centered system. In addition, with moderate pricing, it ensures
educational access without creating class barriers.
○ ‘Lifelong Learning Organizers (LLOs)
The British government established a consulting agency to set national strategies for
non-formal education for adults in the 21 st century that would contribute to the welfare
and prosperity of society. Among the agenda items is promoting the connection among
different learning episodes (DIUS 2008; Vavoula & Sharples 2009: 82, recitation). It aims
to integrate various learning experiences in different contexts by combining
technologies, knowledge and learning resources, while capturing, connecting, organizing
and recycling learning episodes. This process is called “lifelong learning organizers
(LLOs).” It can be defined as a system which helps a learner organize and integrate
his/her meaning records of lifetime by categorizing the activities, knowledge and
materials of his/her learning, which took place at different time periods and places into
specific learning topics (Vavoula & Sharples 2009).
LLOs’ can be a very effective tool for autonomous and self-regulating learners. The
LLO system is based on a technology that connects learning behaviors, learning episodes ,
and learning projects to their contexts and content. In today’s ubiquitous age, the LLO
system is expected to promote self-directed learning while allowing people to organize
the acquired knowledge, learning experiences and resources. However, since people do
not use the technologies following the method given by a system developer, the diversity
of learners and the linking of their learning outcomes to sharing systems have become
issues. Moreover, educational issues which are more than technological issues, such as
the revision and removal of learning records, an issue of a new replacement learning
record system need to be continually researched.
The ubiquitous society has not yet been completely formed, but it can be realized
under through the advancement of information technology, nanotechnology and
biotechnology, which can be applied to everyday objects. In the ubiquitous society three
prospects for lifelong learning will require attention from related scientific and practical
fields.
Firstly, they have to be approached not from the perspective of lifelong learning
technologies, regulations and rationales, but from an educational rationale. Ways where
a ubiquitous learning environment can contribute towards a learner’s autonomous
development and growth need to be explored. When seeking the development of ulearning, development based on the educational rationale focusing on the learner’s
development and growth needs to be sought, rather than one based on an economic
rationale.
Secondly, attention needs to be given to the educational issues within technological
progress, apart from positive prospects for lifelong learning. The examples of this are
class background, age, educational gaps and information literacy. The issue of equal
access to educational resources also needs critical attention.
Thirdly, there is a need for standards and research on various educational and ethical
issues for active educational interaction. Issues such as copyright with regard to
promoting information sharing, personal information protection, and standards for
managing various personal learning records need to be discussed.
3. Lifelong learning trends in Korea
In Korea, u-learning is getting attention as a new educational paradigm to raise
autonomous and creative talent for the information-oriented society. Some research has
suggested a paradigm change will remake education (OECD, 2001, Jae Bun Lee et al,
2006, Hye Young Lee et al, 2008).
The OECD predicts that demand for individual
educational services to develop autonomous and creative learning abilities in diverse
consumers will rise and schools will develop into regional learning centers, re-schooling,
networking and de-schooling (OECD, 2001). Future schools will have an open model of
recurrent education. It has been suggested that systems such as academic years and
grades will no longer be used, and that the school system will be based on programs
rather than schools: schools will be used as learning centers connected by networks
(Hye Young Lee et al, 2008). Meanwhile, lifelong learning will play a very positive role in
responding to numerous integral factors within society, such as formal and non-formal
education for adults (Soo Myoung Chang, 2009). Korea has ample IT infrastructure is an
IT developed country, while the government promotes the expansion of continuous
connectivity, higher speed, and the development of experimental models. After the
1990s, Korea has had a comprehensive strategic plan for computerization, such as eKorea and u-Korea (ubiquitous Korea). The establishment of a ubiquitous environment
at the national level was conducted along with the basic strategies of u-Korea in 2005,
and post-ubiquitous strategies are being made and applied in each field. The master plan
for information technology was developed and applied to education after 1996 in order
to develop a nation with more creative talents. It has three levels: level 1 built the
infrastructure (1996~2000); level 2 taught ICT use (2001~2005), and; level 3
introduced the u-learning system (2006~2010). In level 3, the advancement of
infrastructure and information services and the expansion of computerization in lifelong
learning and higher education were tried on the basis of the results of the first two levels,
but the result has been unsatisfactory. Following the reorganization of the Ministry of
Education, Technology and Science, the master plan for information technology in
education was changed into a basic plan of information technology in education and
science (the Ministry of Education, Science and Technology, 2010.5.25). A taskforce
focusing on educational advancements, an educational consulting agency in the
Presidential Council for Future and Vision, has suggested a need for ‘establishment of ulearning support system’ as a mid- and long-term measure, together with the expansion
plan of EBSi to promote educational competitiveness. In November, 2010, the Council
proposed a policy direction through a national u-learning TF for research of a national
vision and strategy for ubiquitous learning in 2020, including policies to support higher
education and lifelong learning by establishing a national u-learning system. However,
there are not many cases of ubiquitous lifelong learning. The u-learning environment
indicates a pan-national educational environment anywhere and at any time. This
requires the establishment of infrastructure for an accessibility and operational
environment for educational contents. In regard to accessibility, an education safety net
that provides support for neglected people and as well as educational welfare should be
considered. When compared with u-learning for entrance examinations, u-learning in
job training and lifelong learning for self-improvement are scarce both in the public and
private sectors.
(1) Public sector
○Gyeonggi lifelong learning, homelearn
Homelearn (http://www.homelearn.go.kr) is an e-learning website provided by
Gyeonggi women’s development center that aims to help 12 million residents of the
province reach self-realization by capacity building and self-improvement. It opened in
May, 2010 and offers 500 free educational programs in five thematic areas: foreign
languages, liberal arts, computer skills, management, and leadership. It has 150
thousands members. It has integrated existing e-learning programs in lifelong learning
in 31 cities. In addition, it operates educational systems and courses that can satisfy
various learners’ needs in different cities and towns.
(Figure1)Website of Homelearn
All educational content is free and since of its quality is high, the satisfaction rate of
users is relatively high. With diverse educational courses and learning methods, the
number of its users is expected to rise. Though it is an e-learning system based on the
web, from May 2011 its service expanded to mobile devices. Gyeonggi province also
opened a website, Gyeonggi lifelong learning portal path, in December 2010, offering
services such as lifelong learning news, information of provincial lifelong learning
institutes, and a pool of lecturers, lifelong learning clubs, and lifelong learning
volunteering.
○ KOCW
KOCW (Korea Open CourseWare, http://www.kocw.net/) is a project for Korean open
courseware, based on MIT’s OCW project. OCW is a kind of noblesse oblige, with an aim
to share the ample knowledge of people. In 2002, MIT lectures were open to the public.
Even though the intellectual properties and privilege of lecturers might get lower, in
order to attract talented students and develop creative ideas that the society demands
MIT tried to handle change in the educational environments of the Internet age through
OCW. As a result, 1,900 subjects in 35 departments within MIT went public.
In Korea, Korea University and Kyunghee University started first in 2007, and in 2008
Korea Open courseware consortium was established. To date, 23 higher education
institutes are the members of KOCW.
(Figure 2) Website of KOCW
KOCW’s services have three purposes:
1) Expanding educational opportunities and promoting competitiveness in higher
education by establishing a national e-learning community;
2) Spreading the culture of knowledge-sharing among universities while sharing good
lectures and the examples of excellent lecturers on the web; and
3) Expanding learners’ right for learning and lifelong learning opportunities by
improving access to college lectures.
The Korea education and research information service started KOCW in December
2007 as a pilot project. As of May 2011, 1,785 college lectures from Korea, 634 college
lectures from overseas, and 119,392 general educational resources are offered. Teaching
and learning materials offered by KOCW can be freely accessed through an application
for both android and apple phones.
(Figure3) Screen shot of the KOCW application
KOCW also offers OpenAPI and allows the development of applications and services to
access their open lecture videos, domestic academic journals, overseas academic
journals, and Ph.D. theses, without having to access their website. This allows external
developers and users to search and share KOCW data in standard XML form.
(2) Educational Sector
In Korea, there are about 400 community colleges, universities, open universities and
polytechnics. Many colleges have already installed Wi-Fi and Wibro networks inside
their campuses creating a free wireless environment for mobile devices. Students can
access vast amounts of information, from cafeteria menus to job vacancies, and perform
multiple tasks, including registration, downloading lecture notes and submitting
assignments. The use of u-learning in cyber universities is particularly active. At present,
Korea National Open University and other 18 cyber universities as well as two distance
lifelong learning facilities offer u-learning. All approved by the Ministry and can award
associate bachelor degre. From 2001, lifelong learning facilities, which had operated as
distance education, were given legal permission to be converted into cyber universities,
following the passage of higher education and private school acts in October, 2007. In
2009, lifelong learning facilities became higher education institutes according to the
Higher Education Act; they can operate a graduate school, exchange credits, and award
dual degrees with overseas universities. They are also eligible to apply for the
government funds and projects. As of April 2011, four cyber universities offer special
graduate programs.
<Table 1> Operational statistics of cyber universities
Division
Cyber
Universi
ty
Progra
m
University
Max. No. of Students Max. no. of students for special
(2011)
graduate programs (2011)
Admission
Total
Year of
No. of
Establish graduate
ment
schools
Admissio
n
2011
2
140
2011
2
593
2011
2
48
2010
3
290
Kyunghee Cyber
university
2009(2001)
3,000
11,600
Kukje digital University
2009(2003)
840
3,090
Daegu Cyber University
2009(2002)
1,500
5,500
Busan Digital University 2009(2002)
1,000
3,600
Cyber Korea Foreign
Language University
2009(2004)
1,600
6,400
Seoul Cyber University
2009(2001)
3,000
10,900
Sejong Cyber University 2009(2001)
1,800
5,860
2009(2002)
1,500
5,500
2009(2001)
2,500
10,000
Korea cyber University
2009(2001)
1,650
6,600
Hanyang Cyber
University
2009(2002)
3,150
11,750
Hwasin Cyber University 2009
360
1,080
Digital Seoul Cultural Art
2010(2002)
University
990
3,980
Seoul Digital University
2010(2001)
3,000
12,000
Global cyber University
2010
635
1,125
Open Cyber University
2011(2001)
1,000
4,000
Youngjin Cyber
University
2010(2002)
1,200
2,000
Korea Welfare Cyber
University
2011
500
500
29,225
105,485
Bachelo Wongwang digital
University
r
Degree Korea cyber University
Associat
e
bachelo
r degree
Year of
establishment
(Year of
foundation)
Total: 18 universities
Distance Lifelong
Learning facilities
BA
Youngnam Cyber
2001
university
600
2,400
Ass. BA
World Cyber
University
1,300
2,600
3,840
14,690
Total: 2 universities
2001
(Figure 4) Smart phone applications of cyber universities
Since cyber universities manage teaching and learning through distance education,
they have been actively using e-learning technologies as a student service. Not only do
they allow students to download lectures as MP3 format, they also support the
preparation and revision of lectures through PDA/PMP/UMPC. In addition, students can
access their class schedule and grades, and register for class through their mobile
devices. Furthermore, some cyber universities offer educational contents through IPTV
educational service channels in alliance with IPTV providers.
(3) Private sector
The private sector was quicker to adopt ubiquitous lifelong learning than the public
sector. With the development of mobile communication such as 3G and Wibro, the
introduction of new information communication media such as DMB and IPTV, and the
development of diverse information communication devices such as PMP, MID, netbooks
and smart phones, old content is being converted for dissemination and new content
created for new devices.
<Table 2> Cases of ubiquitous lifelong learning private educational services
Company
Div
Servic
e
Service
period
Service contents
Subject
Device
Every
employee
PDA
Characteristics
KT
In-company
training
2003~p
resent
Supplying a PDA to
every employee and free
membership to Nespot
Services to teach
languages, leadership, job
skills
Making a website for
wireless network and
acquiring professional
licenses
Citibank
In-company
training
2010~
present
Teaching employees
finance-related expertise
Every
employee
Smart
phone
SNS
connection
May
2005~
present
Portable satellite DMB
service
English and Chinese
language learning
programs
Adults
Satellit
e DMB
audio
3times per
day for 10
minutes or less
IPTV
July
2007~
present
IPTV commercial
service through KT mega
TV’s two-way educational
channel
Daily English for adults,
TOIEC lectures, Basic
English for children
Adults,
children
Twoway
IPTV
Due to the
legislative
issues of IPTV,
there is a delay
for commercial
use
Reuter
s
News
Englis
h
Jan.
2010~
present
Learning English
through Reuters Business
News
Smart
phone
OPIc
BASIC
Jan.
2010~
present
1:1 English speaking
test
Smart
phone
Englis
h Bean
Dec.
2009~
present
English conversation
with topics in current
issues for working adults
Adults
Smart
phone
Webpaper(
Metro)
Differentiate
d service for
different
devices,
synchronization
SKT
PDA
Servic
e
PDA
Sync
servic
e
2002,
2003
Nate PDA Service
Comprehensive package
product at iHandygo site
Adults
PDA
Service
suspension due
to low profit
making
MP3&
PMP
Sync
2005
Due to the widespread
of MP3players and PMP,
offer free download
service to existing users
Adults
MP3,
PMP
Security
problem(No. of
download)
Satelli
te
DMB
Winglish.c
om
Chungda
m
learning
Ubion
Telsk
Language
education(con
tract, BtoC)
Languages
Business
managementrelated
contents
service(contra
ct, BtoC)
Language
education(Con
tract)
PSP
Nespo
tlearni
ng
servic
e
PMP
use
B-L
servic
e
2006
Offering a PSP nespot
service
Adults
PSP
Due to
technical
limitations such
as encoding, it
went as far as
the pilot
program
Nov.
2006~A
ug. 2007
Decrease in learners’
complaints who could not
often access learning
websites
Offering language
learning contents such as
TOEIC, English
Adults
(Staff)
PMP
Low rate of
learning
completion
conversation, Chinese
conversation
2007(bu
t not yet
operatio
nal)
Resolving problems
occurring in mobile phones
Replacing elementary,
middle, high school
textbooks, distance
learning application
Students,
adults
e-Book
reader
Newly
developed
Melon
langua
ge
learni
ng
servic
e
2007~
present
Expanding content
areas from music service
with a fixed monthly fee,
and offering EBS language
contents
Students,
adults
Mobile
phone
20, 000
users per
month
Nate
learni
ng
servic
e
~presen
t
Offering various
content, such as text
messages and videos
Students,
adults
Mobile
phone
Using VM for
more
complicated
services
IPTV
April,20
10~
present
IPTVservice for school
use, offering content for
main curriculum, afterschool programs,
extracurricular activities
Elementary
and middle
school
students
IPTV
USB type setup box
2004
Expanding mobile
learning service areas,
Based on e-Learning
educational service
management know-how,
support wired and
wireless integrated
educational services
PDA
Service
suspension due
to lack of profit
and service
areas
Wibro
ubiqui
tous
learni
ng
servic
e
2007~
present
Establishing a Wibro
network and expanding
users
Business management
in liaison with KT,
Content service in
foreign languages and
liberal arts
PDA,
Wibro
phone
Expanding
Wibro network
areas and
developing
content remain
as future tasks
Credu
applic
ation
(Mobil
e
learni
ng
trainin
g
institu
te)
2011~
present
Offering an exclusive
application for credu
educational services
eBook
servic
e
SK
Telecom
LG U+
Mobile phone
BtoC
Service(exclud
ing e-book
service)
School
education
Mobist
mobile
learni
ng
servic
e
Credu
Educational
service(contra
ct)
BtoC service
Adults
Adults
Smart
phone
IPTV service provides its own operate educational channels. Some channels offer
learning methods in tandem with the web. One distinguishing factor is that they do not
provide the same teaching and learning environment since they embrace varying
accessibilities for different devices and offer services through media integration.
Educational content is offered through mobile devices, and Q&A, interaction and
evaluation are conducted on the web. At present, mainly language education and job
training are offered.
III. Research Method
1. Literature review and case study
A literature review was conducted on the concepts and scope of ubiquitous lifelong
learning, u-learning systems and e-learning needs analysis. To identify trends and
conditions of ubiquitous lifelong learning in Korea, a case study was conducted in the
public sector and private sectors while investigating the Korea’s ubiquitous learning
policymaking process.
2. Survey questionnaire
Survey questionnaires by type were designed to investigate e-learning and lifelong
learning participation by learners, lifelong educators and professors, and to conduct an
e-learning needs analysis for the ubiquitous environment. The survey questionnaire for
learners consisted of four sections: respondents’ profile and e-learning accessibility,
their difficulties, participation, and suggestions. The survey questionnaire for lifelong
educators and professors had three sections: respondent’s profile and program
management, suggestions, and future plans. To ensure the validity of the survey,
questions were modified and supplemented after gathering opinions of experts in each
field.
3. Survey and analysis
A survey was conducted on lifelong program managers, professors and learners to
identify u-learning conditions in Korea’s lifelong learning centers and to conduct a needs
analysis. Collected data was analyzed with SPSS (17.0). The subjects of the survey were
three groups (learners, lifelong educators, and professors) so different questionnaires
for each group were designed. The questions were designed to identify u-learningrelated characteristics for each group, however some of the questions overlapped. Hence,
the learner questionnaire was examined first, and the overlapping questions in the
lifelong educator and professor questionnaire were later addressed and analyzed in it.
Analysis models used were ANOVA and t-test, which conduct a frequency analysis
and cross-sectional analysis, and examine differences in averages. For the questions
allowing overlapping answers, a multiple response data analysis was conducted , and
through the cross-sectional analysis difference in response with regard to background
variables was compared. For statistical significance, the significance probability was set
as 0.05. However, for the multiple response data analysis where the distribution of
groups was difficult to define, the significance probability did not apply. A general trend
could be interpreted, however, from the result of the cross-sectional analysis.
IV. Result analysis
1. Research subjects
The total number of the survey respondents was 298, among whom 80.9% were
learners. The numbers of lifelong educators and professors were 20 (6.7%) and 37
(12.4%), respectively. Although their numbers were quite small, their ratios showed
great diversity in background. Still, the small sampling size made it difficult to secure
accuracy in the statistical significance of the result or to generalize some of the
responses, so analyses focused on the learners’ result data.
<Table 3>Descriptive statistics of research subjects
(Unit: person, %)
Group
Division
Male
Gender
Female
Below 34 years
Age
35~44
45~54
Learner
Lifelong
educator
Professor
Total
95
2
11
108
(39.4)
(10.0)
(29.7)
(36.2)
146
18
26
190
(60.6)
(90.0)
(70.3)
(63.8)
87
9
19
115
(36.3)
(45.0)
(51.4)
(38.7)
90
11
13
114
(37.5)
(55.0)
(35.1)
(38.4)
41
0
4
45
(17.1)
(0.0)
(10.8)
(15.2)
Above 55
Total
22
0
1
23
(9.2)
(0.0)
(2.7)
(7.7)
241
20
37
298
(100.0)
(100.0)
(100.0)
(100.0)
In terms of gender, 190 (63.8%) were women. The ratio was even higher among
lifelong educators: (90% were women), with 70.3% of professors being women. In terms
of age, learners who were under 34 years old and between 35 and 44 were 38.7% and
38.4% respectively, which comprised most of the respondents. Those 55 years old or
older comprised only 7.7%. In groups, professor respondents who were 34 years old or
less accounted for about half (51.4%), and together with the respondents who were
between 35 and 44 years old (35.1%) accounted for 86.5%. Those under 45 represented
the lion’s share. For lifelong educators, all but one of the respondents was 44 years old
or younger.
In order to show a detailed characteristic of learners who accounted for a large
portion of the total respondents, the following table with academic qualifications,
occupational type, the size of company and average monthly income with a cross sectional analysis is given. According to the statistical analysis, 48.7% of learners had
bachelor degrees and 27.5% of them had master degrees or higher.
<Table 4>Descriptive statistics of learners
(Unit: person, %)
Academic qualification
Division
Administrative
Professional
Clerical
Service·technical
Occupation
Student
House wife
Others
Total
High school
diploma
Bachelor
degree
Master degree
and above
Total
2
9
5
16
(12.5)
(56.3)
(31.3)
(100.0)
4
17
31
52
(7.7)
(32.7)
(59.6)
(100.0)
11
22
9
42
(26.2)
(52.4)
(21.4)
(100.0)
7
15
2
24
(29.2)
(62.5)
(8.3)
(100.0)
4
9
4
17
(23.5)
(52.9)
(23.5)
(100.0)
22
20
0
42
(52.4)
(47.6)
(0.0)
(100.0)
1
10
2
13
(7.7)
(76.9)
(15.4)
(100.0)
51
102
53
206
(24.8)
(49.5)
(25.7)
(100.0)
Below 10
Between 11~49
Size of
company
Between 50~99
Between 100~999
1,000 and more
Total
Below 2million won
Between 2~4 million
Average
monthly
income
won
4million won and more
Total
9
27
3
39
(23.1)
(69.2)
(7.7)
(100.0)
8
13
10
31
(25.8)
(41.9)
(32.3)
(100.0)
3
11
6
20
(15.0)
(55.0)
(30.0)
(100.0)
4
15
6
25
(16.0)
(60.0)
(24.0)
(100.0)
2
12
27
41
(4.9)
(29.3)
(65.9)
(100.0)
26
78
52
156
(16.7)
(50.0)
(33.3)
(100.0)
17
28
7
52
(32.7)
(53.8)
(13.5)
(100.0)
19
43
29
91
(20.9)
(47.3)
(31.9)
(100.0)
10
23
17
50
(20.0)
(46.0)
(34.0)
(100.0)
46
94
53
193
(23.8)
(48.7)
(27.5)
(100.0)
In occupational types, 25.2% of the total learners were engaged in professional work,
followed by 20.4% who were either clerks or housewives. About one-quarter of learners
(26.3%) worked in companies with more than 1,000 employees. The percentage of
respondents who worked in companies with less than 50 employees was 44.9%, which
shows that the size of the learners’ companies was very diverse. Lastly, in average
monthly income, between 2 million and 4 million won was the highest with 47.2% , while
those earning average monthly incomes below 2 million won and above 4 million won
were both about 27%.
The lifelong educator and professors’ distribution of background variables is as
following (though there were far fewer respondents, their background variables were
diverse).
<Table 5>Descriptive statistics of lifelong educators and professors
(Unit: Person, %)
Division
2 years ( or 3 years) and
below
Years of
work
experience 4 years (or 5 years)and
below
Group
Lifelong educator
Professor
Total
5
(25.0)
15
(40.5)
20
(35.1)
8
(40.0)
10
(27.0)
18
(31.6)
4years ( or 5years)and
more
7
(35.0)
12
(32.4)
19
(33.3)
Total
20
(100.0)
37
(100.0)
57
(100.0)
University-affiliated
institutes
4
(20.0)
·
4
(20.0)
16
(80.0)
·
16
(80.0)
20
(100.0)
·
20
(100.0)
Korea National Open
University
·
6
(16.2)
6
(16.2)
Cyber universities
·
11
(29.7)
11
(29.7)
·
7
(18.9)
7
(18.9)
Others
·
13
(35.1)
13
(35.1)
Total
·
37
(100.0)
37
(100.0)
Type of
lifelong Public and municipal
learning centers
institutes
Total
Professor’s
Corporate educational
place of
institutes
work
First of all, in the years of work experience for lifelong educators the ratio of 4 years
or less, in other words the work experience for 2 or 3 years was the highest with 40.0%.
In terms of work experience, 40% of lifelong educators had worked four years or less,
while 35% had worked more than 4 years. The average work experience of professors
was less than that of the lifelong educators: 40.5% of the professors had less than two
years of work experience and the professors with more than two years of work
experience accounted for 59.5%, compared to lifelong educators with 75%.
In terms of workplace, 16 lifelong educators, 80% of the total respondents, worked in
public or municipal centers. The percentage of lifelong educators working in the
university-affiliated institutes was only 20, while 45.9% of professors worked at
universities.
The results for both groups showed a diversity of backgrounds.
2. Needs analysis and result data
Taking account of the characteristics of the above respondents, the result data of the
needs analysis focused on the learners (who accounted for a majority of the
respondents), though when needed the result data of the lifelong educators and
professors was compared.
The result data of the needs analysis in e-learning for lifelong learning in the
ubiquitous environments is presented in three categories: 1) e-learning participation; 2)
awareness and readiness for ubiquitous environments; and 3) needs of e-learning in the
ubiquitous environment.
1) E-learning participation
(1) E-learning experience
Not every learner is engaged in e-learning. Some learners may not have any
experience of e-learning and others may have had the experience but not recently. The
following table shows the extent of learners’ learning experience before analyzing their
e-learning participation.
<Table 6>E-learning experience (1)
Division
Gender
Age
(Unit: person, %)
Recent
Experience
experience
(more than one No experience
(within the past
year)
year)
Total
Male
75
(78.9)
14
(14.7)
6
(6.3)
95
(100.0)
Female
103
(70.5)
33
(22.6)
10
(6.8)
146
(100.0)
Total
178
(73.9)
47
(19.5)
16
(6.6)
241
(100.0)
34 years and
younger
63
(72.4)
19
(21.8)
5
(5.7)
87
(100.0)
35~44 years
65
(72.2)
19
(21.1)
6
(6.7)
90
(100.0)
45~54years
31
(75.6)
6
(14.6)
4
(9.8)
41
(100.0)
55 years and
older
18
(81.8)
3
(13.6)
1
(4.5)
22
(100.0)
Total
177
(73.8)
47
(19.6)
16
(6.7)
240
(100.0)
High school
diploma
41
(71.9)
7
(12.3)
9
(15.8)
57
(100.0)
89
(72.4)
30
(24.4)
4
(3.3)
123
(100.0)
48
(78.7)
10
(16.4)
3
(4.9)
61
(100.0)
178
(73.9)
47
(19.5)
16
(6.6)
241
(100.0)
Bachelor degree
Academic
qualification Master degree or
higher
Total
(Significance,
probability)
2.40
(.301)
2.35
(.885)
13.18
(.010)
The overwhelming majority (93.4%) of learners had e-learning experience, 73.9% of
them within the past year. There was not much difference in e-learning experience
between genders. Depending on whether their e-learning experience was within the past
one year, there is a difference of 8% between men and women. However, when looking
solely at whether or not they had had e-learning experience, and the results for both
men and women were similar. (Combined, only 6.6% of learners had not had e-learning
experience.)
There were a few differences in age and academic qualifications. The experience ratio
of e-learning among learners 55 years old and older was the highest, and the experience
ratio of e-learning among the learners between 45 and 54 (where they reach the peak in
social activity) was lowest. Furthermore, the higher the learner’s academic qualification
is, the higher the rate of e-learning participation is. Among the learners who only have
high school diplomas, 15.8% of them did not have any experience of participating in e learning, exceeding the average of 6.6%. The difference in academic qualification is
shown to be meaningful according to the level of significance, which is .05.
(2) Frequented e-learning educational websites
E-learning educational websites most frequented were distance university websites
(28.1%), such as Korea National Open University, and cyber universities, followed by
distance academies with e-learning (14.8%). About one-tenth (11.7%) of the learners
frequented the municipal institutes and affiliated educational centers.
In terms of gender, the ratio of women using the distance universities (32.4%) is much
higher than that of men (20.7%). This ratio is also higher than the ratios of men taking
classes in the distance academies (16.6%) or taking online lectures from general
universities (13.3%).
There seemed to be an age-related difference according to one’s capacity to use the
online environment. The learners who are 45 years or older often use the online
educational programs and websites of the municipal institutes and affiliated educational
centers, whereas the ratio of the learners who are 44 years or younger have higher
usage of distance academies or the educational program of non-profit organizations. A
similar trend is found in terms of academic qualification. The lower one’s academic
qualification is, the higher the rate of using the websites of distance universities, which
can be deemed as the basic e-learning program. On the other hand, learners with higher
academic qualifications are shown to use the distance academies and the websites of
non-profit organizations apart from the programs offered by the distance universities.
In the field of occupations, there is no significant characteristic trend, but the ratio
among those who work in the service industries and technical fields, or are housewives,
using the programs offered by the distance universities was shown to be higher while
the learners in administrative (17.9%) or clerical positions (17.9%) or that are
professionals (16.7%) use the distance academies more.
In terms of average monthly income, the lower income is, the higher the rate of using
websites of distance universities. There were no other significant differences.
<Table 7>Frequented e-learning educational websites (Overlapping response)
Division
Male
(Unit: Person, %)
Public
NonMunicipal /affi educati ona
General
profi t
Public
liated
l welfare Di stance
Di stance
Othe
uni versi t
pri vate
T o t al
sector educati onal
and
uni versi ty
academy
rs
y
i nsti tu
i nsti tutes
cultural
tes
centers
12
15
7
30
19
24
16
22
145
(8.3)
(10.3)
(4.8)
(20.7)
(13.1)
(16.6)
(11.0) (15.2) (100.0)
21
(8.5)
31
(12.6)
22
(8.9)
80
(32.4)
24
(9.7)
34
(13.8)
24
(9.7)
33
(8.4)
34years or
10
younger
(7.9)
35~44year 15
s of age
(9.6)
45~54 of
6
Age
age or
(8.6)
younger
55 years or
2
older
(5.3)
33
Total
(8.4)
High school
7
diploma
(7.2)
Bachelor
17
(8.7)
Academic degree
Qualificatio Master
9
n
degree or
(9.1)
higher
33
Total
(8.4)
Administrati
4
ve
(14.3)
6
Professional
(8.3)
9
Clerical
(13.4)
Service·Tech
3
nical
(7.9)
Occupation
0
Student
(0.0)
3
Housewife
(4.3)
1
Other
(5.0)
26
Total
(8.0)
4
Below 10
(7.4)
Size of
Between
company
3
11~49 or
(By no. of
(6.0)
less
employees)
Between
9
50~99 or
(21.4)
46
(11.7)
9
(7.1)
19
(12.1)
29
(7.4)
7
(5.6)
13
(8.3)
110
(28.1)
22
(17.5)
44
(28.0)
43
(11.0)
12
(9.5)
17
(10.8)
58
(14.8)
29
(23.0)
24
(15.3)
40
33
392
(10.2) (8.4) (100.0)
20
17
126
(15.9) (13.5) (100.0)
12
13
157
(7.6) (8.3) (100.0)
12
(17.1)
5
(7.1)
26
(37.1)
10
(14.3)
4
(5.7)
6
(8.6)
1
70
(1.4) (100.0)
6
(15.8)
46
(11.8)
14
(14.4)
23
(11.7)
4
(10.5)
29
(7.4)
8
(8.2)
14
(7.1)
18
(47.4)
110
(28.1)
34
(35.1)
61
(31.1)
4
(10.5)
43
(11.0)
10
(10.3)
19
(9.7)
1
(2.6)
58
(14.8)
7
(7.2)
36
(18.4)
1
(2.6)
39
(10.0)
12
(12.4)
13
(6.6)
2
(5.3)
33
(8.4)
5
(5.2)
13
(6.6)
9
(9.1)
7
(7.1)
15
(15.2)
14
(14.1)
15
(15.2)
15
15
99
(15.2) (15.2) (100.0)
46
(11.7)
3
(10.7)
5
(6.9)
7
(10.4)
5
(13.2)
2
(6.9)
10
(14.5)
3
(15.0)
35
(10.8)
5
(9.3)
29
(7.4)
1
(3.6)
5
(6.9)
6
(9.0)
3
(7.9)
4
(13.8)
3
(4.3)
1
(5.0)
23
(7.1)
2
(3.7)
110
(28.1)
6
(21.4)
17
(23.6)
13
(19.4)
15
(39.5)
8
(27.6)
27
(39.1)
6
(30.0)
92
(28.5)
20
(37.0)
43
(11.0)
2
(7.1)
10
(13.9)
5
(7.5)
3
(7.9)
5
(17.2)
9
(13.0)
2
(10.0)
36
(11.1)
9
(16.7)
58
(14.8)
5
(17.9)
12
(16.7)
12
(17.9)
4
(10.5)
5
(17.2)
7
(10.1)
3
(15.0)
48
(14.9)
6
(11.1)
40
(10.2)
3
(10.7)
9
(12.5)
7
(10.4)
3
(7.9)
3
(10.3)
7
(10.1)
2
(10.0)
34
(10.5)
5
(9.3)
5
(10.0)
6
(12.0)
11
(22.0)
5
(10.0)
9
(18.0)
7
4
50
(14.0) (8.0) (100.0)
5
(11.9)
5
(11.9)
8
(19.0)
4
(9.5)
4
(9.5)
6
1
42
(14.3) (2.4) (100.0)
Gender
Female
Total
11
247
(4.5) (100.0)
33
(8.4)
4
(14.3)
8
(11.1)
8
(11.9)
2
(5.3)
2
(6.9)
3
(4.3)
2
(10.0)
29
(9.0)
3
(5.6)
38
(100.0)
391
(100.0)
97
(100.0)
196
(100.0)
392
(100.0)
28
(100.0)
72
(100.0)
67
(100.0)
38
(100.0)
29
(100.0)
69
(100.0)
20
(100.0)
323
(100.0)
54
(100.0)
less
Between
100~999s
More than
1,000
4
(10.0)
3
(5.3)
5
(12.5)
2
(3.5)
4
(10.0)
2
(3.5)
10
(25.0)
9
(15.8)
6
(15.0)
3
(5.3)
7
(17.5)
13
(22.8)
2
2
40
(5.0) (5.0) (100.0)
8
17
57
(14.0) (29.8) (100.0)
Total
23
(9.5)
22
(9.1)
19
(7.8)
58
(23.9)
27
(11.1)
39
(16.0)
28
27
243
(11.5) (11.1) (100.0)
Below 2
million won
8
(9.5)
7
(8.3)
7
(8.3)
26
(31.0)
11
(13.1)
13
(15.5)
8
(9.5)
2~4million
9
Average s
(6.7)
monthly
8
income More than 4
million won
(9.2)
14
(10.4)
8
(5.9)
40
(29.6)
11
(8.1)
25
(18.5)
14
14
135
(10.4) (10.4) (100.0)
10
(11.5)
7
(8.0)
21
(24.1)
12
(13.8)
8
(9.2)
11
10
87
(12.6) (11.5) (100.0)
25
(8.2)
31
(10.1)
22
(7.2)
87
(28.4)
34
(11.1)
46
(15.0)
33
28
306
(10.8) (9.2) (100.0)
Total
4
84
(4.8) (100.0)
(3) Places for e-learning
The following table is a brief summary of places where e-learning usually takes place.
Most, 69.3%, engage in e-learning at home and only 19.7% reported engaging in elearning at work.
<Table 8>Places for e-learning (1)
(Unit: Person, %)
Gender
Age
Division
Relevant
educational
institutes
House
Workplace
Male
5
(5.4)
49
(53.3)
29
(31.5)
8
(8.7)
1
(1.1)
92
(100.0)
Female
4
(2.7)
116
(79.5)
18
(12.3)
6
(4.1)
2
(1.4)
146
(100.0)
Total
9
(3.8)
165
(69.3)
47
(19.7)
14
(5.9)
3
(1.3)
238
(100.0)
34 years or
younger
4
(4.8)
49
(58.3)
25
(29.8)
5
(6.0)
1
(1.2)
84
(100.0)
35~44 years of
age
3
(3.3)
65
(72.2)
15
(16.7)
7
(7.8)
0
(0.0)
90
(100.0)
45~54 years of
age
1
(2.4)
32
(78.0)
5
(12.2)
2
(4.9)
1
(2.4)
41
(100.0)
55 years or older
1
(4.5)
19
(86.4)
1
(4.5)
0
(0.0)
1
(4.5)
22
(100.0)
Total
9
(3.8)
165
(69.6)
46
(19.4)
14
(5.9)
3
(1.3)
237
(100.0)
High school
diploma
2
(3.5)
45
(78.9)
4
(7.0)
5
(8.8)
1
(1.8)
57
(100.0)
5
(4.1)
91
(75.2)
17
(14.0)
6
(5.0)
2
(1.7)
121
(100.0)
2
(3.3)
29
(48.3)
26
(43.3)
3
(5.0)
0
(0.0)
60
(100.0)
9
(3.8)
165
(69.3)
47
(19.7)
14
(5.9)
3
(1.3)
238
(100.0)
Bachelor degree
Academic
qualification Master degree or
higher
Total
Inside
Others
vehicles
Total
(Significance
probability)
19.25
(.001)
17.37
(.136)
30.91
(.000)
In terms of gender, more women (79.5%) answered that they mostly engage in e learning at home than men (53.3%), while more men (31.5%) engage in e-learning at
work than women (12.3%). Considering that 33.9% of the women respondents are
housewives, it is obvious that their place for learning is home. Given that 90% of the
men respondents are office workers, it is understandable that the rate of men who
engage in e-learning at work cannot help but be high. When looked at the data in terms
of age, the most common place for e-learning for all age groups was home. Nevertheless,
upon looking at the rate of those who answered that they mostly engaged in e-learning
at work, its rate increased in the younger groups.
In terms of academic qualification, 80% of the learners with only high school diplomas
or bachelor degrees answered they engaged in e-learning at home, whereas the rate of
the learners with more than master degrees is only 48.3%. More than two-fifths (43.3%)
of those with master degrees or higher engage in e-learning at work. This indicates that
with higher academic qualifications, learners are highly likely to ha ve more time and
equipment for e-learning at their disposal at work.
the responses in regard to average monthly income are summarized in the table below.
<Table 9>Places for e-learning(2)
House
Workplace
Inside
vehicle
Others
Total
Administrative
0
(0.0)
10
(62.5)
6
(37.5)
0
(0.0)
0
(0.0)
16
(100.0)
Professional
2
(4.0)
26
(52.0)
17
(34.0)
5
(10.0)
0
(0.0)
50
(100.0)
Clerical
2
(4.8)
23
(54.8)
16
(38.1)
1
(2.4)
0
(0.0)
42
(100.0)
Service·Technical
0
(0.0)
18
(75.0)
3
(12.5)
3
(12.5)
0
(0.0)
24
(100.0)
Student
1
(5.9)
14
(82.4)
0
(0.0)
2
(11.8)
0
(0.0)
17
(100.0)
Housewife
0
(0.0)
40
(95.2)
1
(2.4)
0
(0.0)
1
(2.4)
42
(100.0)
Other
2
(15.4)
8
(61.5)
2
(15.4)
0
(0.0)
1
(7.7)
13
(100.0)
Total
7
(3.4)
139
(68.1)
45
(22.1)
11
(5.4)
2
(1.0)
204
(100.0)
Below 2 million
won
3
(5.8)
39
(75.0)
5
(9.6)
5
(9.6)
0
(0.0)
52
(100.0)
2~4 million won
1
(1.1)
55
(61.1)
27
(30.0)
6
(6.7)
1
(1.1)
90
(100.0)
More than 4million
won
2
(4.1)
33
(67.3)
13
(26.5)
0
(0.0)
1
(2.0)
49
(100.0)
Total
6
(3.1)
127
(66.5)
45
(23.6)
11
(5.8)
2
(1.0)
191
(100.0)
Division
Occupation
Average
monthly
income
(Unit: person, %)
Relevant
educational
institutes
(Significance
probability)
57.70
(.000)
14.79
(.063)
The rate of the learners who have administrative, professional or clerical jobs
engaging in e-learning at work was higher (37.5%, 34% and 38.1% respectively).
Students or housewives (82.4% and 95.2% respectively) engage in e-learning at home,
which is logical since in most cases they do not have jobs.
The rate of the learners with higher average monthly incomes engaging in e -learning
at work was higher. This is not because of the effect of an income difference, but it is a
difference that shows how one’s age and occupation make their workplace suitable for elearning.
(4) Primary e-learning tools
E-learning tools, which the learners use primarily, were examined. The result was
organized into the following table according to gender, age, and academic qualification.
According to the data, 73.9% of the learners were taking e-learning classes on their
personal computers. Only 19.7% were laptop users. What’s more, only 2.5% of the
learners used smart phones, which have been reported to be very popular recently, for
e-learning.
<Table 10> Primary e-learning tools (1)
(Unit: Person, %)
Gender
Age
Division
Personal PC
Laptop
Smart phone
Others
Total
Male
66
(71.7)
20
(21.7)
6
(6.5)
0
(0.0)
92
(100.0)
Female
110
(75.3)
27
(18.5)
0
(0.0)
9
(6.2)
146
(100.0)
Total
176
(73.9)
47
(19.7)
6
(2.5)
9
(3.8)
238
(100.0)
34 years or
younger
57
(67.1)
22
(25.9)
2
(2.4)
4
(4.7)
85
(100.0)
35~44 years of
age
68
(75.6)
17
(18.9)
3
(3.3)
2
(2.2)
90
(100.0)
Between 45~54
32
(80.0)
5
(12.5)
1
(2.5)
2
(5.0)
40
(100.0)
55 years or older
19
(86.4)
2
(9.1)
0
(0.0)
1
(4.5)
22
(100.0)
Total
176
(74.3)
46
(19.4)
6
(2.5)
9
(3.8)
237
(100.0)
High school
diploma
43
(76.8)
9
(16.1)
2
(3.6)
2
(3.6)
56
(100.0)
91
(74.6)
22
(18.0)
3
(2.5)
6
(4.9)
122
(100.0)
42
(70.0)
16
(26.7)
1
(1.7)
1
(1.7)
60
(100.0)
176
(73.9)
47
(19.7)
6
(2.5)
9
(3.8)
238
(100.0)
Bachelor degree
Academic
qualification Master degree or
higher
Total
(Significance
probability)
15.59
(.001)
7.02
(.635)
3.77
(.708)
When examined it in terms of gender, there seems to be a statistically significant
difference of .05 in the cross-analysis, however in reality, the difference is not so big.
None of the 146 female learners used smart phones for e-learning, whereas 6 (6.5%)
male learners used smart phones for e-learning. This may demonstrate that male
learners may have adjusted to the use of smart phones, a relatively new device and
learning method. In the event of the expansion of mobile learning with smart phones,
efforts should be made to encourage them among female learners.
The type of computer a learner uses changes dramatically with regard to age. For
More than 80% of older people use personal computers, while young and middle-aged
people are more likely to use laptops (25.9% and 18.9% respectively). This shows that
younger learners engage in e-learning activities even when they are on the go.
There is a similar trend aligned with academic qualifications. Of those who have not
studied past high school , 76.8%, compared with 70% among those with master’s
degrees or higher. Learners with higher academics are also more likely to use laptops
(26.7%).The following table shows learners’ occupations and average monthly income.
<Table 11>Main e-learning tools (2)
Personal
PC
Laptop
Smart phone
others
Total
Administrative
14
(87.5)
2
(12.5)
0
(0.0)
0
(0.0)
16
(100.0)
Professional
37
(72.5)
10
(19.6)
3
(5.9)
1
(2.0)
51
(100.0)
Clerical
33
(78.6)
8
(19.0)
0
(0.0)
1
(2.4)
42
(100.0)
Service·Technical
14
(58.3)
7
(29.2)
1
(4.2)
2
(8.3)
24
(100.0)
Student
9
(52.9)
6
(35.3)
1
(5.9)
1
(5.9)
17
(100.0)
Housewife
35
(83.3)
6
(14.3)
0
(0.0)
1
(2.4)
42
(100.0)
Other
10
(76.9)
3
(23.1)
0
(0.0)
0
(0.0)
13
(100.0)
Total
152
(74.1)
42
(20.5)
5
(2.4)
6
(2.9)
205
(100.0)
Less than 2
million won
38
(73.1)
9
(17.3)
1
(1.9)
4
(7.7)
52
(100.0)
Between 2~4
million won
68
(75.6)
17
(18.9)
4
(4.4)
1
(1.1)
90
(100.0)
More than 4
million won
36
(72.0)
13
(26.0)
0
(0.0)
1
(2.0)
50
(100.0)
Total
142
(74.0)
39
(20.3)
5
(2.6)
6
(3.1)
192
(100.0)
Division
Occupation
Average
monthly
income
(Unit: Person, %)
(Significance
Probability)
17.29
(.503)
8.59
(.198)
A surprisingly high percentage of learners in administrative, professional and clerical
positions use personal computers (72.5%~87.5%), while those in service and technical
jobs reported the highest use of laptops. Students and those who work in services or
technical fields use laptops relatively more frequently since they do not have their own
office or are often on the move.
A correlation between income and PC use was also found: as income rises use of PCs
declines and usage of laptops increases slightly. It appears that learners’ learning is
greatly affected by their occupation and academic qualifications and that the high price
of laptops has an effect.
In summary, more than 90% of respondents had had an e-learning experience. Most
are engaged in e-learning through websites or distance or cyber universities through
their personal computers at home. The higher one’s academic qualification is, the higher
the rate of e-learning participation is. Moreover, young learners engage in e-learning
activities even while on the move.
2) Awareness of and preparedness for the ubiquitous environment
In order to identify awareness and preparedness for the ubiquitous environment,
those surveyed were asked about: (1) e-learning devices they currently use (2) elearning devices they will purchase within a year (3) the degree to which they are
familiar with the Internet and electronic devices, (4) the extent to which they use
electronic devices for time and resource management.
(1) E-learning devices that they currently use
Learners were told to mark all the devices that they currently have, and a multiple
response analysis was conducted to compare different responses from background
variables. The most common devices were desktop computers (21.5%), followed by
laptops (18.8%), MP3 players (17.0%), and mobile phones (16.6%).
In terms of gender, slightly more women had desktop computers (23.8%), mobile
phones (20.9%) and mp3 players (19.2%) than men. Far more men, however, had smart
phones than women. While women owned more conventional e-learning devices, men
were more likely to own e-learning-related devices such as iPods, Tablets, smart pads
and e-books than women.
No great difference in terms of age was found between the use of desktop computers
and the use of laptops. However, smart phone use was higher among those 34 years of
age or younger, while older respondents were more likely to use conventional phones. .
<Table 12> E-learning devices that learners currently have (overlapping responses) (1)
Division
(Unit: person, %)
EDesktop
Mobil
Tabl
Lapto
Smartp MP3play
Smar Book Other
compute
e
iPod et PDA PMP
Total
p
hone
er
t pad reade s
r
phone
PC
r
Male
79
(18.7)
76
47
(18.0) (11.1)
60
(14.2)
60
(14.2)
21
16
10
19
19
14
(5.0) (3.8) (2.4) (4.5) (4.5) (3.3)
Female
125
(23.8)
102
110
(19.4) (20.9)
34
(6.5)
101
(19.2)
13
7
4
20
6
(2.5) (1.3) (.8) (3.8) (1.1)
Total
204
(21.5)
178
157
(18.8) (16.6)
94
(9.9)
161
(17.0)
34
23
14
39
25
17
(3.6) (2.4) (1.5) (4.1) (2.6) (1.8)
34 years
or
younger
66
(18.0)
68
48
(18.6) (13.1)
47
(12.8)
65
(17.8)
17
15
2
15
15
8
(4.6) (4.1) (.5) (4.1) (4.1) (2.2)
Between
35~44
81
(23.0)
69
63
(19.6) (17.9)
29
(8.2)
59
(16.8)
10
4
5
15
9
7
(2.8) (1.1) (1.4) (4.3) (2.6) (2.0)
Between
45~54
37
(24.7)
26
29
(17.3) (19.3)
11
(7.3)
24
(16.0)
6
3
4
7
(4.0) (2.0) (2.7) (4.7)
55 years
or older
19
(24.7)
14
17
(18.2) (22.1)
6
(7.8)
13
(16.9)
1
1
3
2
0
1
(1.3) (1.3) (3.9) (2.6) (0.0) (1.3)
Total
203
(21.5)
177
157
(18.7) (16.6)
93
(9.8)
161
(17.0)
34
23
14
39
25
17
(3.6) (2.4) (1.5) (4.1) (2.6) (1.8)
High
school
diploma
48
(24.9)
31
40
(16.1) (20.7)
14
(7.3)
33
(17.1)
2
1
5
13
3
3
(1.0) (.5) (2.6) (6.7) (1.6) (1.6)
Bachelor’ 105
Academic s degree (22.8)
qualificatio
Master’s
n
51
degree
(17.3)
or higher
89
88
(19.3) (19.1)
36
(7.8)
85
(18.5)
16
7
5
18
7
(3.5) (1.5) (1.1) (3.9) (1.5)
58
29
(19.7) (9.8)
44
(14.9)
43
(14.6)
16
15
4
8
15
11
(5.4) (5.1) (1.4) (2.7) (5.1) (3.7)
204
(21.5)
178
157
(18.8) (16.6)
94
(9.9)
161
(17.0)
34
23
14
39
25
17
(3.6) (2.4) (1.5) (4.1) (2.6) (1.8)
Gender
Age
Total
1
(.7)
3
(.6)
1
(.7)
3
(.7)
422
1
(100.0
(.2)
)
526
1
(100.0
(.2)
)
948
2
(100.0
(0.2)
)
366
0
(100.0
(0.0)
)
352
1
(100.0
(.3)
)
150
1
(100.0
(.7)
)
77
0
(100.0
(0.0)
)
945
2
(100.0
(0.2)
)
193
0
(100.0
(0.0)
)
460
1
(100.0
(.2)
)
295
1
(100.0
(.3)
)
948
2
(100.0
(0.2)
)
The following table is the response result based on average monthly income. There
seems to be differences across different occupations. There was little difference between
housewives and learners of other occupation groups when it came to commonly used
devices, such as PCs, laptops, mobile phones, and MP3 players. However, with regard to
state-of-the art devices such as smart phones, iPods, tablet PCs, and smart pads, few
housewives used any.
Results based on average monthly income seem to reflect the financial burden of the
price of devices used for e-learning. There was no difference across the different income
groups for commonly used devices, but the rate of ownership for high-end devices
correlated with rising income.
<Table 13> E-learning devices that learners currently have (overlapping responses) (2)
Division
(Unit: person, %)
Desktop
Mobil Smar
Tabl
ELapto
MP3
Smar
Othe
comput
e
tpho
iPod et PDA PMP
Book
Total
p
player
t pad
rs
er
phon ne
PC
read
e
er
Administrative
14
(20.9)
67
13
12
6
11
2
1
1
2
3
2
0
(19.4) (17.9) (9.0) (16.4) (3.0) (1.5) (1.5) (3.0) (4.5) (3.0) (0.0) (100.0)
Professional
48
(20.5)
234
47
26
32
32
13
6
5
7
12
6
0
(20.1) (11.1) (13.7) (13.7) (5.6) (2.6) (2.1) (3.0) (5.1) (2.6) (0.0) (100.0)
Clerical
36
(20.3)
177
29
28
16
32
6
9
3
11
4
3
0
(16.4) (15.8) (9.0) (18.1) (3.4) (5.1) (1.7) (6.2) (2.3) (1.7) (0.0) (100.0)
19
(23.2)
82
15
13
10
13
1
2
2
4
0
3
0
(18.3) (15.9) (12.2) (15.9) (1.2) (2.4) (2.4) (4.9) (0.0) (3.7) (0.0) (100.0)
13
(20.0)
65
12
13
6
12
2
1
0
5
1
0
0
(18.5) (20.0) (9.2) (18.5) (3.1) (1.5) (0.0) (7.7) (1.5) (0.0) (0.0) (100.0)
Housewife
36
(26.3)
Other
10
(20.0)
137
27
34
5
27
2
0
0
5
1
0
0
(19.7) (24.8) (3.6) (19.7) (1.5) (0.0) (0.0) (3.6) (.7) (0.0) (0.0) (100.0)
5
50
7
8
5
7
4
2
0
1
1
0
(10.0
(14.0) (16.0) (10.0) (14.0)
(8.0) (4.0) (0.0) (2.0) (2.0) (0.0) (100.0)
)
Total
176
(21.7)
Service·Techni
cal
Occupatio
n
Student
Less than 2
million won
Between 2~4
Average million won
monthly
income More than 4
million won
Total
43
(22.8)
150
134
80
134
31
23
13
34
22
15
0
812
(18.5) (16.5) (9.9) (16.5) (3.8) (2.8) (1.6) (4.2) (2.7) (1.8) (0.0) (100.0)
189
37
36
16
37
3
1
3
7
2
4
0
(19.6) (19.0) (8.5) (19.6) (1.6) (.5) (1.6) (3.7) (1.1) (2.1) (0.0) (100.0)
78
(21.1)
370
69
56
38
58
17
11
4
17
15
7
0
(18.6) (15.1) (10.3) (15.7) (4.6) (3.0) (1.1) (4.6) (4.1) (1.9) (0.0) (100.0)
44
(20.7)
213
38
31
25
32
10
10
6
8
5
4
0
(17.8) (14.6) (11.7) (15.0) (4.7) (4.7) (2.8) (3.8) (2.3) (1.9) (0.0) (100.0)
165
(21.4)
772
144
123
79
127
30
22
13
32
22
15
0
(18.7) (15.9) (10.2) (16.5) (3.9) (2.8) (1.7) (4.1) (2.8) (1.9) (0.0) (100.0)
(2) E-learning devices learners will purchase within one year
Learners were told to check all devices that they plan to purchase, and a multiple
response analysis was conducted to compare different responses from background
variables. A largest number of learners (30.7%) answered that they intended to
purchase smart phones, followed by smart pads (14.0%) or e-book readers (14.0%).
Plans to purchase devices that have been launched quite recently, may show that
commonly used devices lay the groundwork for a shift from the e-learning era to the
mobile learning era.
In terms of gender, women exhibited a trend towards buying laptops (10.8%), smart
phones (36.5%) or e-book devices that was higher than men, while men have more
demand for e-learning devices that have been launched recently, such as tablet PCs
(15.0%) and smart pads (27.5%). Comparing this data with the devices that the learners
currently possess, it appears that female learners are one step behind male learners
when it comes to buying and using the state-of-the-art devices.
In terms of age, those 55 or older showed a similar trend to those 34 years or younger
towards buying state-of-the-art devices such as tablet PCs, smart pads, e-book devices.
Across academic qualifications, there were many learners with only high school
diplomas that wanted to buy laptops (21.7%), smart phones (26.1%) or e-book devices
(21.7%), whereas those with master’s degrees or higher constitute 75% of the learners
that wanted to buy smart phones (25.0%), tablet PCs (15.0%) or smart pads (35.0%).
<Table 14> E-learning devices that the learners will purchase within one year (Overlapping response) (1)
(Unit: person, %)
EDesktop
Mobil
Lapto
Smartp MP3
Tablet
Smar Book Oth
Division
compute
e
iPod
PDA PMP
Total
p
hone player
PC
t pad Reade ers
r
phone
r
1
3
1
8
1
1
6
1
3
11
4
0
40
Male
(2.5)
(7.5) (2.5) (20.0) (2.5) (2.5) (15.0) (2.5) (7.5) (27.5) (10.0) (0.0) (100.0)
Gender
Age
27
(36.5)
0
(0.0)
74
5
8
1
4
5
12
1
(6.8) (10.8) (1.4) (5.4) (6.8) (16.2) (1.4) (100.0)
1
(0.9)
35
(30.7)
1
(0.9)
114
6
14
2
7
16
16
1
(5.3) (12.3) (1.8) (6.1) (14.0) (14.0) (0.9) (100.0)
4
(8.9)
0
(0.0)
13
(28.9)
0
(0.0)
45
1
5
1
2
8
7
1
(2.2) (11.1) (2.2) (4.4) (17.8) (15.6) (2.2) (100.0)
0
(0.0)
4
(9.8)
0
(0.0)
13
(31.7)
0
(0.0)
1
(6.3)
1
(6.3)
1
(6.3)
7
(43.8)
1
(6.3)
2
0
(16.7) (0.0)
2
(16.7)
0
(0.0)
1
(0.9)
35
(30.7)
1
(0.9)
41
4
6
0
2
7
5
0
(9.8) (14.6) (0.0) (4.9) (17.1) (12.2) (0.0) (100.0)
16
1
1
1
1
0
1
0
(6.3) (6.3) (6.3) (6.3) (0.0) (6.3) (0.0) (100.0)
2
0
2
0
1
3
0
12
(16.7
(0.0) (16.7) (0.0)
(8.3) (25.0) (0.0) (100.0)
)
114
6
14
2
7
16
16
1
(5.3) (12.3) (1.8) (6.1) (14.0) (14.0) (0.9) (100.0)
5
0
(21.7) (0.0)
6
(26.1)
0
(0.0)
1
(4.3)
1
(1.4)
Female
3
(4.1)
Total
4
(3.5)
11
(9.6)
3
(6.7)
34 years
or
younger
Between
35~44
Between
45~54
55 years
or older
0
(0.0)
Total
4
(3.5)
High
school
diploma
Bachelor’
Academic s degree
qualificatio
Master’s
n
degree or
higher
Total
1
(4.3)
8
0
(10.8) (0.0)
11
(9.6)
2
(2.8)
6
(8.5)
1
(1.4)
24
(33.8)
1
(5.0)
0
(0.0)
0
(0.0)
5
(25.0)
4
(3.5)
11
(9.6)
1
(0.9)
35
(30.7)
23
1
0
1
3
5
0
(4.3) (0.0) (4.3) (13.0) (21.7) (0.0) (100.0)
3
10
2
5
6
10
1
71
(4.2) (14.1) (2.8) (7.0) (8.5) (14.1) (1.4) (100.0)
2
20
0
3
0
1
7
1
0
(10.0
(0.0)
(15.0) (0.0) (5.0) (35.0) (5.0) (0.0) (100.0)
)
1
(0.9)
114
6
14
2
7
16
16
1
(5.3) (12.3) (1.8) (6.1) (14.0) (14.0) (0.9) (100.0)
The following table is the response result based on the average monthly income. As
can be seen from the table, it is difficult to grasp the trend across occupations due to the
lack of respondents. Therefore, the trend analysis of the occupation groups has been left
out.
Furthermore, there is quite a lack of respondents in the average monthly income field.
Nonetheless, there is one interesting point. If the income is low, the desire to buy a
smart phone is higher while the desire to buy a smart pad gets lower. This makes quite a
contrast. Although the numbers of users are increasing for both devices, it costs more to
buy smart pads. So just by looking at the data result, learners make a rational choice
between smart phones and smart pads while taking their incomes into consideration.
<Table 15> E-learning devices that the learners will purchase within a year (Overlapping response) (2)
(Unit: person, %)
Deskto
Mobi Smart MP3
Tablet
Smart
E- Othe
Division
Laptop
iPod
PDA PMP
Total
p
le phone play
PC
pad Book rs
compu
ter
Administra
tive
phon
e
0
(0.0)
0
(0.0)
Professiona
1
(7.1)
l
0
(0.0)
Clerical
2
(7.7)
Service·Tec
0
(0.0)
Occupati hnical
on
0
Student
(0.0)
er
Reader
1
0
2
0
0
1
0
2
1
0
7
(14.3
(0.0) (28.6) (0.0) (0.0) (14.3) (0.0)
(28.6) (14.3) (0.0) (100.0)
)
2
0
4
0
2
0
0
4
1
0
14
(14.3
(0.0) (28.6) (0.0)
(14.3) (0.0) (0.0) (28.6) (7.1) (0.0) (100.0)
)
3
0
9
0
0
3
0
0
3
6
0
26
(11.5) (0.0) (34.6) (0.0) (0.0) (11.5) (0.0) (0.0) (11.5) (23.1) (0.0) (100.0)
0
(0.0)
0
3
0
0
(0.0) (75.0) (0.0) (0.0)
0
(0.0)
0
0
1
(0.0) (0.0) (25.0)
0
(0.0)
0
4
(0.0) (100.0)
1
(6.3)
0
3
0
0
(0.0) (18.8) (0.0) (0.0)
1
(6.3)
2
1
4
3
1
16
(12.5
(6.3)
(25.0) (18.8) (6.3) (100.0)
)
Housewife
0
(0.0)
2
3
0
7
0
0
(14.3
(21.4) (0.0) (50.0) (0.0)
(0.0)
)
0
1
(0.0) (7.1)
0
(0.0)
1
(7.1)
0
14
(0.0) (100.0)
Other
0
(0.0)
2
0
1
0
0
(66.7) (0.0) (33.3) (0.0) (0.0)
0
(0.0)
0
0
(0.0) (0.0)
0
(0.0)
0
(0.0)
0
3
(0.0) (100.0)
Total
3
(3.6)
9
0
29
0
4
(10.7) (0.0) (34.5) (0.0) (4.8)
7
(8.3)
1
4
14
12
1
84
(1.2) (4.8) (16.7) (14.3) (1.2) (100.0)
1
(4.2)
0
1
4
4
0
24
(0.0) (4.2) (16.7) (16.7) (0.0) (100.0)
Less than 2
2
million won (8.3)
Between
1
2~4 million
(2.9)
Average
won
monthly
More than
income
0
4 million
(0.0)
won
3
Total
(4.1)
1
(4.2)
0
10
0
1
(0.0) (41.7) (0.0) (4.2)
5
0
11
0
1
5
0
0
6
5
0
34
(14.7) (0.0) (32.4) (0.0) (2.9) (14.7) (0.0) (0.0) (17.6) (14.7) (0.0) (100.0)
1
(6.7)
0
5
0
1
(0.0) (33.3) (0.0) (6.7)
1
(6.7)
2
0
3
2
0
15
(13.3
(0.0)
(20.0) (13.3) (0.0) (100.0)
)
7
0
26
0
3
7
0
3
13
11
0
73
(9.6) (0.0) (35.6) (0.0) (4.1) (9.6) (0.0) (4.1) (17.8) (15.1) (0.0) (100.0)
(3) Level of familiarity with electronic networks and devices, such as the Internet
and mobile phones
The learners’ level of familiarity with electronic networks and devices needed for elearning, such as the Internet, mobile phones and laptops has been examined. Their level
of familiarity was measured on a five point Likert scale (1 for not familiar at all and 5 for
quite familiar) and their averages were calculated to analyze differences in background
variables. According to the data result, 75% of the learners answered that they were
familiar with the electronic networks and devices such as the Internet, mobile phones
and laptops. The average was 4.02, showing relatively a high level of familiarity.
In terms of gender, the rate of learners quite familiar with the devices was high in
men (42.1%) while the rate of the learners with average familiarity was high in women
(28.1%). However, there was no great difference between men (4.3%) and women (6.2%)
in the rates of learners who thought negatively of their familiarity with the electronic
devices. Men and women showed a high level of familiarity: 4.18 for men and 3.92 for
women although the difference in the level of familiarity across gender was significant
statically, in reality, it is not.
There was a significant difference across the age groups. In general, as the learner’s
age increases, the learners responded that they were less familiar with the devices, and
the rate of the learners who were quite familiar with the devices decreased dramatically.
Upon examining their real familiarity on the scale, learners 34 years or younger rated
4.32, while learners who 55 years or older was 3.50. The age, which demarcates this
difference, is about 45. A correlation was also found in terms of academic qualifications,
with higher academic qualifications corresponding to a higher level of familiarity with
electronic devices. Among learners with master’s degrees or higher, no one was
unfamiliar with the devices. From the result of an average analysis, the familiarity level
of the learners that have only high school diplomas was only 3.63 but the familiarity
level of those with bachelor’s degrees or higher exceeds 4.
<Table 16> Level of familiarity with electronic networks and devices (1)
(Unit: Person, %)
Division
Gender
Age
Not
Quite
familiar Unfamiliar Average familiar
familiar
at all
Total
Average F(t) Value
(Standard (Significance
deviation) probability)
Male
1
(1.1)
3
(3.2)
14
(14.7)
37
(38.9)
40
(42.1)
95
(100.0)
4.18
(0.87)
Female
2
(1.4)
7
(4.8)
32
(21.9)
64
(43.8)
41
(28.1)
146
(100.0)
3.92
(0.90)
Total
3
(1.2)
10
(4.1)
46
(19.1)
101
(41.9)
81
(33.6)
241
(100.0)
4.02
(0.90)
34 years or
younger
2
(2.3)
0
(0.0)
8
(9.2)
35
(40.2)
42
(48.3)
87
(100.0)
4.32
(0.83)
Between 35~44
0
(0.0)
4
(4.4)
17
(18.9)
40
(44.4)
29
(32.2)
90
(100.0)
4.04
(0.83)
Between 45~54
1
(2.4)
2
(4.9)
15
(36.6)
17
(41.5)
6
(14.6)
41
(100.0)
3.61
(0.89)
55 years or
older
0
(0.0)
4
(18.2)
6
(27.3)
9
(40.9)
3
(13.6)
22
(100.0)
3.50
(0.96)
Total
3
(1.3)
10
(4.2)
46
(19.2)
101
(42.1)
80
(33.3)
240
(100.0)
4.02
(0.90)
High school
diploma
1
(1.8)
5
(8.8)
15
(26.3)
29
(50.9)
7
(12.3)
57
(100.0)
3.63
(0.88)
2
(1.6)
5
(4.1)
22
(17.9)
49
(39.8)
45
(36.6)
123
(100.0)
4.06
(0.93)
9.65
(.000)
0
(0.0)
0
(0.0)
9
(14.8)
23
(37.7)
29
(47.5)
61
(100.0)
4.33
(0.72)
a<b,c
3
(1.2)
10
(4.1)
46
(19.1)
101
(41.9)
81
(33.6)
241
(100.0)
4.02
(0.90)
Bachelor’s
Academic degree
qualification Master’s degree
or higher
Total
2.16
(.032)
9.52
(.000)
a>c,d
However, there is little distinct difference or trend across occupation groups. However,
the level of familiarity of housewives (3.69) or those in technical fields (3.71) was
relatively very low when compared with professionals (4.33). Average monthly income
did not significantly affect level of familiarity with electronic devices significantly. Those
earning less than 2 million won had the average score of 4.15, while earning more than 4
million won scored 4.04.
<Table 17> Level of familiarity with electronic networks and devices (2)
(Unit: Person, %)
Division
Total
Average F(t) Value
(Standard (Significance
deviation) probability)
Administrative
0
(0.0)
0
(0.0)
2
(12.5)
10
(62.5)
4
(25.0)
16
(100.0)
4.13
(0.62)
Professional
0
(0.0)
0
(0.0)
8
(15.4)
19
(36.5)
25
(48.1)
52
(100.0)
4.33
(0.73)
Clerical
0
(0.0)
1
(2.4)
4
(9.5)
21
(50.0)
16
(38.1)
42
(100.0)
4.24
(0.73)
Service·Technical
0
(0.0)
3
(12.5)
7
(29.2)
8
(33.3)
6
(25.0)
24
(100.0)
3.71
(1.00)
3.43
(.003)
Student
0
(0.0)
1
(5.9)
2
(11.8)
9
(52.9)
5
(29.4)
17
(100.0)
4.06
(0.83)
b>f
Housewife
0
(0.0)
3
(7.1)
15
(35.7)
16
(38.1)
8
(19.0)
42
(100.0)
3.69
(0.87)
Other
0
(0.0)
1
(7.7)
4
(30.8)
2
(15.4)
6
(46.2)
13
(100.0)
4.00
(1.08)
Total
0
(0.0)
9
(4.4)
42
(20.4)
85
(41.3)
70
(34.0)
206
(100.0)
4.05
(0.85)
Less than 2
million won
0
(0.0)
4
(7.7)
9
(17.3)
14
(26.9)
25
(48.1)
52
(100.0)
4.15
(0.98)
Between 2~4
million won
0
(0.0)
4
(4.4)
16
(17.6)
44
(48.4)
27
(29.7)
91
(100.0)
4.03
(0.81)
More than 4
million won
0
(0.0)
1
(2.0)
14
(28.0)
17
(34.0)
18
(36.0)
50
(100.0)
4.04
(0.86)
Total
0
(0.0)
9
(4.7)
39
(20.2)
75
(38.9)
70
(36.3)
193
(100.0)
4.07
(0.87)
Occupation
Average
monthly
income
Not
Quite
familiar Unfamiliar Average familiar
familiar
at all
0.35
(.703)
(4) The extent to which learners use electronic devices for time and resource
management
Learners were asked the extent to which they use electronic devices for time or
resource management, and the responses measured on a five point Likert scale.
According to the result data, 63.6% used electronic devices for time and resource
management, while 13.8% did not. Men (3.72) used the devices a little more than
women (3.65), but the difference was statistically insignificant.
The situation was different in terms of age. Those 34 years or younger averaged a
score of 3.86 and those 55 years or older averaged only 3.27. Though all age groups had
a scores above average (more than 3), the extent to which learners use electronic
devices rises with decreasing learner age. Across academic qualifications, the extent to
which learners use electronic devices increases with academic qualifications, and there
is a clear distinction between those with high school diplomas and those with degrees .
<Table 18> Extent to which learners use electronic devices for time and resource management
(Unit: Person, %)
Gender
Age
Division
Never
use
Male
2
(2.2)
8
(8.6)
21
(22.6)
45
(48.4)
17
(18.3)
93
(100.0)
3.72
(0.937)
Female
6
(4.1)
17
(11.6)
33
(22.6)
63
(43.2)
27
(18.5)
146
(100.0)
3.60
(1.047)
Total
8
(3.3)
25
(10.5)
54
(22.6)
108
(45.2)
44
(18.4)
239
(100.0)
3.65
(1.005)
34 years or
younger
3
(3.5)
8
(9.4)
12
(14.1)
37
(43.5)
25
(29.4)
85
(100.0)
3.86
(1.060)
Between 35~44
2
(2.2)
9
(10.0)
22
(24.4)
44
(48.9)
13
(14.4)
90
(100.0)
3.63
(0.930)
Between 45~54
1
(2.4)
6
(14.6)
12
(29.3)
19
(46.3)
3
(7.3)
41
(100.0)
3.41
(0.921)
55 years or
older
2
(9.1)
2
(9.1)
8
(36.4)
8
(36.4)
2
(9.1)
22
(100.0)
3.27
(1.077)
Total
8
(3.4)
25
(10.5)
54
(22.7)
108
(45.4)
43
(18.1)
238
(100.0)
3.64
(1.003)
High school
diploma
5
(8.8)
8
(14.0)
17
(29.8)
24
(42.1)
3
(5.3)
57
(100.0)
3.21
(1.048)
3
(2.5)
14
(11.5)
27
(22.1)
51
(41.8)
27
(22.1)
122
(100.0)
3.70
(1.020)
9.14
(.000)
0
(0.0)
3
(5.0)
10
(16.7)
33
(55.0)
14
(23.3)
60
(100.0)
3.97
(0.780)
a<b,c
8
(3.3)
25
(10.5)
54
(22.6)
108
(45.2)
44
(18.4)
239
(100.0)
3.65
(1.005)
Bachelor’s
Academic degree
qualification Master’s degree
or higher
Total
Do not
Often
Average Occasionally
use
use
Average F(t) Value
(Standard (Significance
deviation) probability)
Total
0.88
(.379)
3.10
(.027)
The following table is the brief summary of differences in the response result across
occupations and average monthly incomes. The extent to which electronic devices are
used at work varies according to job. Housewives (3.31) and those working in the
service sector or technical fields (3.31) used the devices relatively sparsely, while those
in administrative positions use them the most (4).
An analysis of the effect of average monthly income on use of electronic devices found
that average monthly income was not a statistically significant factor. Those earning less
than 2 million won scored 3.65 and those earning more than 4 million won scored 3.68.
<Table 19> Extent to which learners use electronic devices for time and resource management
Division
Administrative
Occupation Professional
Clerical
Never
use
Do not
Average
use
Use
Often
use
Total
(Unit: Person, %)
Average F(t) Value
(Standard (Significance
deviation) probability)
0
(0.0)
0
(0.0)
2
(12.5)
12
(75.0)
2
(12.5)
16
(100.0)
4.00
(0.516)
1
(2.0)
3
(6.0)
10
(20.0)
26
(52.0)
10
(20.0)
50
(100.0)
3.82
(0.896)
1
(2.4)
4
(9.5)
9
(21.4)
17
(40.5)
11
(26.2)
42
(100.0)
3.79
(1.025)
2.14
(.050)
Average
monthly
income
Service·Technical
2
(8.3)
4
(16.7)
6
(25.0)
8
(33.3)
4
(16.7)
24
(100.0)
3.33
(1.204)
Student
0
(0.0)
1
(5.9)
7
(41.2)
8
(47.1)
1
(5.9)
17
(100.0)
3.53
(0.717)
Housewife
2
(4.8)
9
(21.4)
9
(21.4)
18
(42.9)
4
(9.5)
42
(100.0)
3.31
(1.070)
Other
0
(0.0)
1
(7.7)
4
(30.8)
4
(30.8)
4
(30.8)
13
(100.0)
3.85
(0.987)
Total
6
(2.9)
22
(10.8)
47
(23.0)
93
(45.6)
36
(17.6)
204
(100.0)
3.64
(0.990)
2
(3.9)
7
(13.7)
13
(25.5)
14
(27.5)
15
(29.4)
51
(100.0)
3.65
(1.163)
1
(1.1)
10
(11.1)
19
(21.1)
48
(53.3)
12
(13.3)
90
(100.0)
3.67
(0.887)
2
(4.0)
4
(8.0)
11
(22.0)
24
(48.0)
9
(18.0)
50
(100.0)
3.68
(0.999)
5
(2.6)
21
(11.0)
43
(22.5)
86
(45.0)
36
(18.8)
191
(100.0)
3.66
(0.991)
Less than 2
million won
Between 2~4
million won
More than 4
million won
Total
0.01
(.986)
As has been shown above, most learners already had devices for e-learning (desktop
computers, 21.5%, laptops, 18.8%, and mobile phones 16.6%) and have a strong desire
to buy smart phones, smart pads or e-book devices within a year (smart phones, 30.7%,
smart pads, 14.0%, e-book devices, 14.0%). The devices which they plan to buy are those
that have been launched recently. Most (75%) of the learners use and are familiar with
the electronic networks and devices such as the Internet, mobile phones and laptops,
and as most of them use these devices for time or resource management (66%), it
appears that e-learning is laying the groundwork for the ubiquitous learning era.
3) E-learning needs for lifelong learning in the ubiquitous environment
Through an analysis of the awareness of and preparedness for the ubiquitous
environment aforementioned, one can see that the groundwork for learning in the
ubiquitous environment is being laid. The question becomes: what kind of e-learning
needs would lifelong learners have in the ubiquitous environment? The e-learning needs
of lifelong learners have been examined in the following order: 1) intention to
participate in lifelong learning through mobile learning; 2) preferred learning method
on the move; 3) intention to use a smart phone application to access e-learning lectures;
4) needs for mobile learning by subjects; 5) learning support needed for e-learning and
mobile learning; and 6) requirements for active e-learning and mobile learning.
(1) Intention to participate in lifelong learning through mobile learning
Learners, lifelong educators and professors were asked whether they would participate
in a lifelong learning course if the opportunity to engage in lifelong learning via mobile
devices was given. Responses were measured on a 5 point Likert scale, with 1 being no
intention to taking part in mobile learning and 5 showing a high inclination to
participate. Based on the frequency response for each category and the average score,
differences across the background variables were analyzed.
<Table 20>Intention to participate in lifelong learning through mobile learning
Group
Division
None
Little
Average
A little
Fairly
Total
Learner
12
(5.0)
32
(13.3)
51
(21.2)
84
(34.9)
62
(25.7)
241
(100.0)
3.63
(1.15)
Lifelong educator
0
(0.0)
1
(5.0)
4
(20.0)
7
(35.0)
8
(40.0)
20
(100.0)
4.10
(0.91)
Professor
0
(0.0)
2
(5.4)
3
(8.1)
13
(35.1)
19
(51.4)
37
(100.0)
4.32
(0.85)
Male
3
(2.8)
13
(12.0)
22
(20.4)
39
(36.1)
31
(28.7)
108
(100.0)
3.76
(1.08)
Female
9
(4.7)
22
(11.6)
36
(18.9)
65
(34.2)
58
(30.5)
190
(100.0)
3.74
(1.15)
34 years or
younger
2
(1.7)
13
(11.3)
22
(19.1)
40
(34.8)
38
(33.0)
115
(100.0)
3.86
(1.06)
Between 35~44
7
(6.1)
13
(11.4)
21
(18.4)
37
(32.5)
36
(31.6)
114
(100.0)
3.72
(1.20)
Between 45~54
2
(4.4)
6
(13.3)
11
(24.4)
14
(31.1)
12
(26.7)
45
(100.0)
3.62
(1.15)
55 years or older
1
(4.3)
3
(13.0)
3
(13.0)
13
(56.5)
3
(13.0)
23
(100.0)
3.61
(1.03)
Total
12
(4.0)
35
(11.7)
58
(19.5)
104
(34.9)
89
(29.9)
298
(100.0)
3.75
(1.13)
Gender
Age
(Unit: Person, %)
Average F(t) Value
(Standard (Significance
deviation) probability)
7.45
(.001)
a<c
0.13
(.900)
0.71
(.546)
Almost two-thirds of respondents (64.8%) reported that they intend to take part in
lifelong learning programs with mobile learning, and the 3.75 score on the Likert scale
was fairly high. The learner participation score was 3.65, and the lifelong educator and
professor participation scores were 4.10 and 4.2, respectively. It is noteworthy that the
learner group scored lowest. Participation should be high among the learners who will
carry out lifelong learning activities rather than those who will provide mobile learning,
yet the expectation and the participation intention of program providers was higher.
Learners who showed the lowest rate of participation intention in mobile learning were
examined across the background variables: 64.8% showed an intention to participate in
lifelong learning programs through mobile learning, with a fairly high score of 3.75.
There was no statistically relevant difference within gender or age groups. The
participation intention scores of men and women were 3.76 and 3.74 respectively. In
terms of age, learners 34 years old or younger showed relatively high participation
intention with 3.86, but this was not much higher than those 55 years old or older
(3.61).
Table 21 summarizes differences across the background variables. Learners with
master’s degrees or higher had the lowest score of 3.46, while those with bachelor’s
degrees had the highest score (3.73). However, the difference was not so large, even
when taking account of those with only high school diplomas.
In terms of occupation, the difference in scores is greater than it is for age groups, but
on the whole remains small. While the scores are high for students and those in the
administrative positions (4.00), participation intention scores for the learners in
professional positions and housewives were relatively low. Just by looking at the scores,
it seems that there are differences among some groups but if the scores and their
occupations are considered together, there seems to be no special trend. It is difficult to
determine the reason for the deviation in scores. It seems that in the case of students,
they are more familiar with mobile devices and furthermore, since they already use
smart phones and smart pads for e-learning programs, they have less participation
intention than other occupation groups.
Lastly, participation intention across average monthly incomes, found that learners
with an income of less than 2 million won had the highest score of participation
intention of 4.06. Those whose incomes are between 2 million and 4 million won (3.45)
and more than 4 million won (3.58) had lower participation intention. This result is
quite unexpected, considering that purchasing and using mobile devices are quite
expensive. Though the financial conditions of the low-income learner group do not
support mobile learning, their expectations for mobile learning is higher than others
groups.
<Table 21>Intention of participating in lifelong learning through mobile learning (learners)
(Unit: Person, %)
Average
F(t) Value
(Standard (Significance
deviation) probability)
Division
None Little Average A little
fairly
Total
High school diploma
3
(5.3)
7
(12.3)
14
(24.6)
19
(33.3)
14
(24.6)
57
(100.0)
3.60
(1.15)
8
(6.5)
14
(11.4)
16
(13.0)
50
(40.7)
35
(28.5)
123
(100.0)
3.73
(1.18)
1
(1.6)
11
(18.0)
21
(34.4)
15
(24.6)
13
(21.3)
61
(100.0)
3.46
(1.07)
12
(5.0)
32
(13.3)
51
(21.2)
84
(34.9)
62
(25.7)
241
(100.0)
3.63
(1.15)
0
(0.0)
0
(0.0)
4
(25.0)
8
(50.0)
4
(25.0)
16
(100.0)
4.00
(0.73)
Bachelor’s degree
Academic
qualification Master’s degree or
higher
Total
Occupation Administrative
1.19
(.307)
0.97
(.446)
Professional
2
(3.8)
8
(15.4)
15
(28.8)
15
(28.8)
12
(23.1)
52
(100.0)
3.52
(1.13)
Clerical
1
(2.4)
8
(19.0)
7
(16.7)
17
(40.5)
9
(21.4)
42
(100.0)
3.60
(1.11)
Service·Technical
2
(8.3)
2
(8.3)
4
(16.7)
9
(37.5)
7
(29.2)
24
(100.0)
3.71
(1.23)
Student
0
(0.0)
0
(0.0)
5
(29.4)
7
(41.2)
5
(29.4)
17
(100.0)
4.00
(0.79)
Housewife
3
(7.1)
6
(14.3)
8
(19.0)
14
(33.3)
11
(26.2)
42
(100.0)
3.57
(1.23)
2
3
(15.4) (23.1)
2
(15.4)
2
(15.4)
4
(30.8)
13
(100.0)
3.23
(1.54)
Other
Average
monthly
income
Total
10
(4.9)
27
(13.1)
45
(21.8)
72
(35.0)
52
(25.2)
206
(100.0)
3.63
(1.14)
Less than 2 million
won
1
(1.9)
2
(3.8)
8
(15.4)
23
(44.2)
18
(34.6)
52
(100.0)
4.06
(0.92)
Between 2~4 million
won
5
(5.5)
18
(19.8)
18
(19.8)
31
(34.1)
19
(20.9)
91
(100.0)
3.45
(1.19)
5.04
(.007)
More than 4 million
won
2
(4.0)
7
(14.0)
15
(30.0)
12
(24.0)
14
(28.0)
50
(100.0)
3.58
(1.16)
a>b
Total
8
(4.1)
27
(14.0)
41
(21.2)
66
(34.2)
51
(26.4)
193
(100.0)
3.65
(1.14)
(2) Preferred learning method on the move
Learning methods available and preferred by the learners on the move are examined
below. Out of eight learning methods, learners were told to select the most preferred
method and according to their preferences, the methods have been arranged.
<Table 22>Preferred learning method on the move
Division
Gender
Reading
Readin
Textboo
Student/pr Do not
Wi-Fi MP3
online
Uploa g other
(Significanc
k/
ofessor
want
Lectur Lectur
education d and student
Total e
Referenc
communic
to
es
es
al
share
s’
probability)
e
ation
study
materials
posting
Male
35
(36.8)
28
(29.5)
6
(6.3)
7
(7.4)
3
(3.2)
4
(4.2)
11
(11.6)
1
95
(1.1) (100.0)
Female
46
(31.5)
47
(32.2)
19
(13.0)
5
(3.4)
3
(2.1)
10
(6.8)
13
(8.9)
3
146
(2.1) (100.0)
Total
81
(33.6)
75
(31.1)
25
(10.4)
12
(5.0)
6
(2.5)
14
(5.8)
24
(10.0)
4
241
(1.7) (100.0)
19
(21.8)
8
(9.2)
6
(6.9)
3
(3.4)
6
(6.9)
10
(11.5)
3
87
(3.4) (100.0)
29
(32.2)
10
(11.1)
3
(3.3)
1
(1.1)
7
(7.8)
7
(7.8)
1
90
(1.1) (100.0)
Between
10
(24.4)
45~54
17
(41.5)
4
(9.8)
3
(7.3)
2
(4.9)
1
(2.4)
4
(9.8)
0
41
(0.0) (100.0)
55 years
6
or older (27.3)
10
(45.5)
3
(13.6)
0
(0.0)
0
(0.0)
0
(0.0)
3
(13.6)
0
22
(0.0) (100.0)
80
(33.3)
75
(31.3)
25
(10.4)
12
(5.0)
6
(2.5)
14
(5.8)
24
(10.0)
4
240
(1.7) (100.0)
18
(31.6)
9
(15.8)
3
(5.3)
3
(5.3)
2
(3.5)
3
(5.3)
3
57
(5.3) (100.0)
43
(35.0)
11
(8.9)
5
(4.1)
1
(.8)
7
(5.7)
13
(10.6)
34 years
32
or
(36.8)
younger
Between
32
(35.6)
35~44
Age
(Unit: Person, %)
Total
High
16
Academic school
(28.1)
qualificati diploma
on
Bachelor 42
’s degree (34.1)
1
(.8)
123
(100.0)
6.64
(.467)
19.16
(.575)
17.73
(.219)
Master’s
degree
23
(37.7)
or
higher
14
(23.0)
5
(8.2)
4
(6.6)
2
(3.3)
5
(8.2)
8
(13.1)
0
61
(0.0) (100.0)
81
(33.6)
75
(31.1)
25
(10.4)
12
(5.0)
6
(2.5)
14
(5.8)
24
(10.0)
4
241
(1.7) (100.0)
Total
Learners’ most preferred method of learning was watching online lectures through
Wi-Fi (36.3%), followed by watching online lectures stored in their mp3 players (31.6%).
This is because various online lectures are being offered and social trend s show the
number of online learners increasing. Moreover, the experience of having prepared for a
university entrance exam through online lectures during middle and high school years
will reduce resistance towards online lectures when these students become adult
learners. In the future, the demand for online lectures is expected to grow.
According to the background variables, male learners were likely to listen to Wi-Fi
lectures (36.8%) more than lectures on their MP3 players (29.5%). This was true of
women as well. They use MP3 players to listen to music. Considering that it is difficult to
download lectures and store them on an MP3 player, this explains the low rate. Across
the age groups, there is no characteristic difference. However, there is a slight difference
of preference in Wi-Fi devices and MP3 players between the those younger than 45 and
the those older than 45: 35% of the learners that were 44 years and younger listened to
the lectures via Wi-Fi, however, the rate decreased to 24~27% among those 45 years or
older. On the other hand, the rate of them listening to the lectures on MP3 players is
between 41 ~45%. The rate of young learners having iPods, smart phones and smart
pads, through which they can use Wi-Fi, was quite high and they were familiar with
using them. On the contrary, the rate of the older learners, those 45 years or older,
having these devices was not only low but since they are not familiar with Wi-Fi
environments, it seems they preferred listening to the lectures on MP3 players.
In terms of academic qualifications, as the learners’ academic qualifications rose, they
preferred streaming service through Wi-Fi to MP3 players. Learners with only high
school diplomas, apart from these two methods, preferred reading textbooks or
reference books on the move (15.8%). Whether or not learners have the devices has a
great effect since they need to have Wi-Fi devices or mp3 players separately.
<Table 23>Preferred learning method on the move
Division
(Unit: Person, %)
Reading
Readin
Textboo
Student/pr Do not
(Significan
Wi-Fi MP3
online
Uploa g other
k/
ofessor
want
ce
lectur lectur
education d and student
Total
Referenc
communica to
probabilit
es
es
al
share
s’
e
tion
study
y)
materials
posting
Administrativ
5
9
(31.3) (56.3)
e
0
(0.0)
0
(0.0)
1
(6.3)
0
(0.0)
1
(6.3)
0
16
(0.0) (100.0)
Professional
21
12
(40.4) (23.1)
5
(9.6)
4
(7.7)
0
(0.0)
5
(9.6)
5
(9.6)
0
52
(0.0) (100.0)
Clerical
14
13
(33.3) (31.0)
3
(7.1)
1
(2.4)
3
(7.1)
2
(4.8)
4
(9.5)
2
42
(4.8) (100.0)
Service·Techni 14
3
(58.3) (12.5)
Occupati cal
on
5
6
Student
0
(0.0)
4
(16.7)
0
(0.0)
0
(0.0)
3
(12.5)
0
24
(0.0) (100.0)
(29.4) (35.3)
3
(17.6)
1
(5.9)
0
(0.0)
1
(5.9)
1
(5.9)
0
17
(0.0) (100.0)
Housewife
7
16
(16.7) (38.1)
10
(23.8)
1
(2.4)
2
(4.8)
2
(4.8)
4
(9.5)
0
42
(0.0) (100.0)
Other
6
3
(46.2) (23.1)
1
(7.7)
0
(0.0)
0
(0.0)
0
(0.0)
2
(15.4)
1
13
(7.7) (100.0)
Total
72
62
(35.0) (30.1)
22
(10.7)
11
(5.3)
6
(2.9)
10
(4.9)
20
(9.7)
3
206
(1.5) (100.0)
Less than 2
million won
20
19
(38.5) (36.5)
3
(5.8)
4
(7.7)
2
(3.8)
1
(1.9)
2
(3.8)
1
52
(1.9) (100.0)
31
26
(34.1) (28.6)
9
(9.9)
6
(6.6)
1
(1.1)
6
(6.6)
11
(12.1)
1
91
(1.1) (100.0)
19
16
(38.0) (32.0)
5
(10.0)
1
(2.0)
2
(4.0)
1
(2.0)
6
(12.0)
0
50
(0.0) (100.0)
70
61
(36.3) (31.6)
17
(8.8)
11
(5.7)
5
(2.6)
8
(4.1)
19
(9.8)
2
193
(1.0) (100.0)
Between 2~4
Average million won
monthly
income More than 4
million won
Total
60.58
(.032)
10.83
(.700)
According to the difference in the response result across the occupation groups given
in the above table, the rate of having Wi-Fi devices is relatively low among students and
housewives. So rather than listening to the online lectures via Wi-Fi, they preferred
listening to the lectures on MP3 players or reading textbooks and reference books.
There is no big difference in the response result across the average monthly incomes.
According to the table above, learners whose average monthly income is less than 2
million won also preferred listening to lectures on the MP3 players (36.5%) or via Wi-Fi
(38.5%). Considering the price of needed devices for their learning, they were expected
to respond differently according to personal finances. However, the result data has
revealed that the personal finance does not affect their choice of learning methods.
Therefore, rather than the price burden for needed devices for e-learning courses,
whether the learners currently have them has a greater effect. Moreover, just as it was
shown in the data of e-learning device ownership, smart phones are not affected by the
average monthly income since they are in common use, and the price of MP3 players has
fallen. Hence, what kind of devices learners have, more than their income, affects their elearning methods.
(3) Intention to use a smart phone application to access e-learning lectures
A more detailed question was asked to identify learners’ intention to use a smart
phone application to access e-learning lectures. The survey was conducted for all
respondents, and their responses were measured on the Likert scale.
<Table 24> Intention to use a Smartphone application to access e-learning lectures
Group
Division
None
Little
Average
A little
fairly
Total
Learner
12
(5.0)
22
(9.2)
50
(20.8)
86
(35.8)
70
(29.2)
240
(100.0)
3.75
(1.12)
Lifelong educator
0
(0.0)
6
(30.0)
5
(25.0)
6
(30.0)
3
(15.0)
20
(100.0)
3.30
(1.08)
Professor
0
(0.0)
0
(0.0)
6
(16.2)
19
(51.4)
12
(32.4)
37
(100.0)
4.16
(0.69)
Male
4
(3.7)
9
(8.4)
21
(19.6)
41
(38.3)
32
(29.9)
107
(100.0)
3.82
(1.07)
Female
8
(4.2)
19
(10.0)
40
(21.1)
70
(36.8)
53
(27.9)
190
(100.0)
3.74
(1.10)
34 years or
younger
4
(3.5)
13
(11.4)
21
(18.4)
41
(36.0)
35
(30.7)
114
(100.0)
3.79
(1.11)
Between 35~44
4
(3.5)
10
(8.8)
20
(17.5)
46
(40.4)
34
(29.8)
114
(100.0)
3.84
(1.06)
Between 45~54
3
(6.7)
2
(4.4)
13
(28.9)
14
(31.1)
13
(28.9)
45
(100.0)
3.71
(1.14)
55 years or older
1
(4.3)
3
(13.0)
7
(30.4)
9
(39.1)
3
(13.0)
23
(100.0)
3.43
(1.04)
Total
12
(4.0)
28
(9.4)
61
(20.5)
111
(37.4)
85
(28.6)
297
(100.0)
3.77
(1.09)
Gender
Age
(Unit: Person, %)
Average F(t) Value
(Standard (Significance
deviation) probability)
4.41
(.013)
b<c
0.61
(.542)
0.95
(.418)
According to the data results, 67.0% of respondents answered that they intend to use
smart phones. On the 5-point scale, the score of 3.77 was relatively a high. According to
the respondents’ groups, professors showed the highest score (4.16) while the learners
had a score of 3.75, similar to the average. When compared with their mobile learning
participation intention score (3.36), they seem to be more positive towards smart
phones. Nonetheless, it is still important to take note that the professors viewed this
more positively than the learners.
On the other hand, there is no great difference among the responses across gender and
age. The difference in the respondents’ intention to use the application between male
(3.82) and female learners (3.74) was only 0.08, which indicates there is almost no
difference. Among responses across age groups, except for the fact that the learners 55
years or older had a relatively low score of 3.43 all the other groups showed their desire
to use the application (3.7~3.9).
This difference was not statistically significant.
Learners’ intention to use the application is examined below.
<Table 25> Intention to use a Smartphone application to access e-learning lectures (learners)
Division
None
Little
High school diploma
4
(7.0)
6
(10.5)
12
(21.1)
19
(33.3)
16
57
(28.1) (100.0)
3.65
(1.20)
7
(5.7)
9
(7.3)
24
(19.5)
47
(38.2)
36
123
(29.3) (100.0)
3.78
(1.12)
1
(1.7)
7
(11.7)
14
(23.3)
20
(33.3)
18
60
(30.0) (100.0)
3.78
(1.06)
Total
12
(5.0)
22
(9.2)
50
(20.8)
86
(35.8)
70
240
(29.2) (100.0)
3.75
(1.12)
Administrative
0
(0.0)
2
(13.3)
1
(6.7)
6
(40.0)
6
15
(40.0) (100.0)
4.07
(1.03)
Professional
1
(1.9)
6
(11.5)
11
(21.2)
14
(26.9)
20
52
(38.5) (100.0)
3.88
(1.11)
Clerical
0
(0.0)
3
(7.1)
6
(14.3)
23
(54.8)
10
42
(23.8) (100.0)
3.95
(0.82)
Service·Technical
2
(8.3)
2
(8.3)
3
(12.5)
7
(29.2)
10
24
(41.7) (100.0)
3.88
(1.30)
Student
1
(5.9)
1
(5.9)
5
(29.4)
6
(35.3)
4
17
(23.5) (100.0)
3.65
(1.11)
Housewife
4
(9.5)
3
(7.1)
15
(35.7)
13
(31.0)
7
42
(16.7) (100.0)
3.38
(1.15)
Other
2
(15.4)
2
(15.4)
1
(7.7)
4
(30.8)
4
13
(30.8) (100.0)
3.46
(1.51)
Total
10
(4.9)
19
(9.3)
42
(20.5)
73
(35.6)
61
205
(29.8) (100.0)
3.76
(1.12)
Less than 2 million
won
3
(5.8)
4
(7.7)
11
(21.2)
15
(28.8)
19
52
(36.5) (100.0)
3.83
(1.18)
Between 2~4 million
won
4
(4.4)
10
(11.1)
15
(16.7)
37
(41.1)
24
90
(26.7) (100.0)
3.74
(1.11)
More than 4 million
won
1
(2.0)
5
(10.0)
12
(24.0)
16
(32.0)
16
50
(32.0) (100.0)
3.82
(1.06)
Total
8
(4.2)
19
(9.9)
38
(19.8)
68
(35.4)
59
192
(30.7) (100.0)
3.79
(1.11)
Bachelor’s degree
Academic
qualification Master’s degree or
higher
Occupation
Average
monthly
income
Average A little
fairly
(Unit: Person, %)
Average F(t) Value
Total (Standard (Significance
deviation) probability)
0.30
(.741)
1.54
(.166)
0.12
(.887)
First of all, in the response result of academic qualifications, all the groups showed
similar scores of 3.7. The learners with high school diplomas had the lowest score of
3.65, but it was not much different from the score of those with bachelor’s degrees and
higher (3.78).
There are slight differences across the occupation groups, but since they are not
statistically significant generalization is difficult. Housewives had the lowest score
(3.38). The score of students was slightly higher (3.65), but is still low in comparison
with the other groups.
As has been discussed above, the reason for this result appears to be the low rate of
smart phone or smart pad ownership among housewives and students.
Lastly, there seems to be almost no difference across average monthly income groups.
The rate of learners whose income is between 2~4 million won is relatively low (3.74),
showing the lowest intention for participation. Learners whose income is less than 2
million won had the highest score (3.83), which indicates that the average monthly
incomes do not create differences.
(4) Needs for mobile learning by subjects
Next, the needs for mobile learning by subjects are examined among learners and
lifelong educators, and on the Likert scale. The result has been summarized into the
following table.
<Table 26> Needs for mobile learning by subject
Division
A du lt
l ite racy
Learner
2.89
(1.31)
3.58
(1.18)
4.00
(1.03)
3.82
(0.88)
3.86
(0.92)
3.55
(1.03)
2.30
(1.17)
3.30
(0.98)
4.15
(0.88)
3.70
(1.13)
4.20
(0.77)
3.80
(1.11)
F Value
(Significance
probability)
1.95
(.053)
1.04
(.297)
-0.63
(.528)
0.56
(.577)
-1.62
(.107)
-1.04
(.301)
Male
2.83
(1.25)
3.39
(1.16)
3.83
(1.16)
3.90
(0.89)
3.84
(0.99)
3.46
(1.07)
2.85
(1.34)
3.66
(1.16)
4.12
(0.92)
3.76
(0.90)
3.91
(0.86)
3.63
(1.02)
-0.08
(.933)
-1.87
(.062)
-2.05
(.042)
1.21
(.226)
-0.55
(.581)
-1.32
(.187)
2.81
(1.27)
3.44
(1.16)
3.99
(1.02)
3.79
(0.98)
3.76
(0.94)
3.33
(1.15)
Between 35~44
2.87
(1.32)
3.67
(1.11)
4.25
(0.81)
3.92
(0.83)
4.01
(0.88)
3.79
(0.92)
Between 45~54
2.88
(1.38)
3.24
(1.30)
3.63
(1.30)
3.61
(0.95)
3.76
(0.92)
3.71
(0.98)
2.82
(1.33)
4.09
(0.92)
3.68
(1.09)
3.77
(0.69)
4.14
(0.83)
3.41
(0.85)
0.05
(.986)
3.26
(.022)
4.65
(.003)
1.23
(.301)
2.11
(.099)
3.86
(.010)
2.84
(1.30)
3.56
(1.17)
4.01
(1.02)
3.81
(0.90)
3.88
(0.91)
3.57
(1.04)
Lifelong
Group educator
Gender Female
T Value
(Significance
probability)
34 years or
younger
Age
(Average, standard deviation)
55
years
or
older
F Value
(Significance
probability)
Total
Aca dem ic
C ul tur e a nd
Ci ti ze n
Job tra ini ng
L ibera l art s
im pro veme nt
ar t
part ici patio n
The most preferred subject area was job training (4.01), followed by liberal arts (3.88),
and culture and art (3.81). The need for adult literacy was quite low (2.84). This shows
that the rate of adults having literacy difficulty was very low and it was based on the
assumption that the learners would not have any literacy difficulties if the learners were
able to engage in mobile learning. Hence, if the mobile learning programs are developed
in the future, the portion of literacy programs should be adjusted accordingly, unlike in
the past.
The result of the respondents’ background variables is as following. First of all, though
the trend across the groups is generally similar, the need for adult literacy seems to
show a statically significant difference. Both groups showed a negative view of adult
literacy, but lifelong educators seemed to hold more negative view of it. In the field of
academic improvement, learners showed a higher score (3.58) than that of lifelong
educators (3.30), while in the fields of liberal arts and citizen participation, lifelong
educators (4.20 and 3.80, respectively) showed higher scores than learners (3.86 and
3.55, respectively). Gender differences were found. Female learners felt a higher need
for job training; the difference with male learners was 0.39, which is statically significant.
Male learners showed a slightly higher score in culture and art, and citizen participation,
but it was not statistically significant.
Differences across age groups were statically significant in the areas of academic
improvement, job training and citizen participation. In the area of academic
improvement, the learners who were 55 years or older seemed to feel the need for it the
most (4.09), while in the area of job training, learners between 35 and 44 years old (4.25)
felt the need of it more than the other groups.
Table 27 examines the differences in learners’ responses across background variables
in detail. According to the result, there are some characteristic responses among the
learners with only high school diplomas. In Korea, considering the rate of high school
entrance is about 90%, a high school diploma is relatively common. For learners older
than 60 years, the high school diploma is not a low academic qualification, however for
most of learners who engage in e-learning, it is likely that a high school diploma is an
academic qualification below average. Therefore, the learners with only a high school
diploma wish that new mobile learning would help them improve their academic
qualifications. Moreover, if learners had some literacy difficulties, they would have some
interest in adult literacy. Hence, as has been seen in the result data, among learners with
only high school diplomas the needs for adult literacy and academic improvement were
higher.
According to the data across the occupation groups, learners in clerical positions
(3.60), service or technical fields (3.71), or students (3.65) and housewives (3.83),
showed a greater demand for academic improvement. But in the area of adult literacy,
learners in the administrative (2.38) and professional positions (2.62) showed little
demand, while the learners in the service sectors or technical fields (3.13) showed more
than average demand. However, the difference across the occupation groups in all the
fields was not statically significant.
Lastly, the differences across average monthly incomes were examined. Among the
learners whose income is less than 2 million won, there was high demand in general,
though this did not create statically significant difference among the groups. In the areas
of adult literacy, academic improvement and citizen participation, there were relatively
high score differences.
<Table 27> Needs for mobile learning by subject (learners)
(Average, standard deviation)
Division
L ibera l
ar ts
Ci ti ze n
part ici patio n
3.04
(1.27)
4.05
(0.93)
4.07
(0.90)
3.84
(0.77)
3.96
(0.89)
3.74
(0.88)
Bachelor’s
degree
2.96
(1.37)
3.63
(1.19)
4.07
(1.09)
3.80
(0.93)
3.82
(0.93)
3.54
(1.03)
2.61
(1.19)
3.05
(1.16)
3.79
(1.00)
3.82
(0.87)
3.84
(0.93)
3.39
(1.14)
F Value
(Significance
probability)
1.97
(.141)
11.86
(.000)
1.76
(.174)
0.04
(.963)
0.51
(.602)
1.66
(.193)
Administrative
2.38
(1.09)
3.13
(1.26)
4.00
(1.03)
3.81
(1.05)
3.75
(1.24)
3.81
(1.11)
Professional
2.62
(1.37)
3.27
(1.25)
4.00
(0.99)
3.87
(0.89)
4.00
(0.86)
3.44
(1.07)
Clerical
2.98
(1.18)
3.60
(1.15)
4.10
(0.91)
3.81
(0.77)
3.64
(0.88)
3.40
(0.96)
Service·Technical
3.13
(1.26)
3.71
(1.43)
3.92
(1.10)
3.92
(0.83)
4.00
(0.98)
3.42
(1.18)
Student
3.06
(1.09)
3.65
(1.00)
3.94
(1.03)
3.82
(0.73)
4.00
(0.71)
3.76
(0.75)
Housewife
2.95
(1.43)
3.83
(0.99)
3.90
(1.05)
3.90
(0.76)
3.95
(0.79)
3.79
(0.95)
Other
3.46
(1.27)
4.08
(1.12)
4.23
(1.17)
3.38
(1.26)
3.62
(1.04)
3.38
(1.26)
1.48
(.187)
1.77
(.107)
0.27
(.950)
0.69
(.657)
1.05
(.397)
1.01
(.418)
3.12
(1.22)
3.96
(1.17)
4.06
(1.02)
3.96
(0.82)
4.10
(0.77)
3.77
(0.96)
Between 2~4
million won
2.75
(1.30)
3.46
(1.20)
4.02
(1.00)
3.78
(0.87)
3.78
(0.92)
3.44
(1.02)
More than 4
million won
F Value
(Significance
probability)
2.68
(1.35)
3.24
(1.13)
3.84
(1.02)
3.78
(0.93)
3.80
(1.01)
3.46
(1.13)
1.79
(.170)
5.15
(.007)
0.71
(.492)
0.82
(.440)
2.21
(.112)
1.84
(.161)
2.89
(1.31)
3.58
(1.18)
4.00
(1.03)
3.82
(0.88)
3.86
(0.92)
3.55
(1.03)
F Value
(Significance
probability)
Less than 2
million won
Average
monthly
income
C ul tur e/
ar t
High school
diploma
Academic
qualification Master’s degree
or higher
Occupation
A du lt
Aca dem ic
Job
l ite racy im pro veme nt tr ai ni ng
Total
(5) Learning support needed for e-learning and mobile learning
Learning support needed to carry out e-learning and mobile learning easily is
summarized in following table. It is limited to learners. According to the result,
questions and answers for learning content, and the need for online tutors that (41.7%)
were indentified the most frequently, followed by solutions for device use and technical
problems, and technical support (25.0%).
According to the result in terms of gender, while demand for the online tutors was 50%
among male learners, among female learners it was only 36.3%. On the contrary, their
demands for technical support (27.4%) and the sharing of learning know-how and
experiences (19.9%) were fairly high. Nonetheless, the difference across gender is not
statistically significant and it is difficult to generalize the result.
<Table28>Learning support needed for e-learning and mobile learning
(Unit: Person, %)
Division
Online
tutor
Male
47
(50.0)
11
(11.7)
14
(14.9)
Female
53
(36.3)
17
(11.6)
Total
100
(41.7)
34 years or
younger
Gender
Age
Sharing
Detailed
learning
explanation
Technical
know-how
of learning
support
and
methods
experiences
Others
Total
20
(21.3)
2
(2.1)
94
(100.0)
29
(19.9)
40
(27.4)
7
(4.8)
146
(100.0)
28
(11.7)
43
(17.9)
60
(25.0)
9
(3.8)
240
(100.0)
39
(45.3)
9
(10.5)
14
(16.3)
20
(23.3)
4
(4.7)
86
(100.0)
Between
35~44
38
(42.2)
8
(8.9)
18
(20.0)
22
(24.4)
4
(4.4)
90
(100.0)
Between
45~54
10
(24.4)
8
(19.5)
7
(17.1)
15
(36.6)
1
(2.4)
41
(100.0)
55 years or
older
12
(54.5)
3
(13.6)
4
(18.2)
3
(13.6)
0
(0.0)
22
(100.0)
Total
99
(41.4)
28
(11.7)
43
(18.0)
60
(25.1)
9
(3.8)
239
(100.0)
High school
diploma
22
(38.6)
6
(10.5)
11
(19.3)
15
(26.3)
3
(5.3)
57
(100.0)
51
(41.8)
16
(13.1)
22
(18.0)
29
(23.8)
4
(3.3)
122
(100.0)
27
(44.3)
6
(9.8)
10
(16.4)
16
(26.2)
2
(3.3)
61
(100.0)
100
(41.7)
28
(11.7)
43
(17.9)
60
(25.0)
9
(3.8)
240
(100.0)
Bachelor’s
degree
Academic
qualification Master’s
degree or
higher
Total
(Significance
Probability)
5.31
(.257)
12.15
(.433)
1.43
(.994)
In the case of age, there is a different response trend between the learners who are 55
years or older and the learners who are younger than 55 years. Among the learners who
are 54 years or younger, the demand for online tutors decreased while the demand f or
technical support increased. Among learners who are 55 years or older, the demand for
online tutors was very high (54.5%) whereas the demand for technical support was only
13.6%. In general, the older one gets the more difficulties he or she has with the new
device use and their technical problems. This is an interesting finding. There is a need to
look into the reason for this trend through future research. One possible answer is that
older learners are in great need of online tutors and they might think can solve technical
problems through online tutors. However, this difference does not hold any statistical
significance so it will be hard to make generalization based on this result.
The difference across the academic qualifications was minute. Higher academic
qualification showed higher demand for online tutors. However, in terms of the response
result there was no significant difference. Demand for technical support and the sharing
of learning know-how did not have a consistent trend with the academic qualifications.
The following table summarizes learners’ response results according to occupation
and average monthly incomes. According to the data, the difference across the
occupation groups was not large. Preferred learning support was different with regard
to the occupations, but as the number of the respondents dispersed, the response rate
could change greatly. Hence, this difference cannot be deemed large.
<Table 29>Learning support needed for e-learning and mobile learning
(Unit: Person, %)
Division
Others
Total
Administrative
8
(50.0)
2
(12.5)
3
(18.8)
3
(18.8)
0
(0.0)
16
(100.0)
Professional
21
(40.4)
3
(5.8)
11
(21.2)
16
(30.8)
1
(1.9)
52
(100.0)
Clerical
18
(42.9)
5
(11.9)
6
(14.3)
13
(31.0)
0
(0.0)
42
(100.0)
Service·Technical
8
(33.3)
4
(16.7)
3
(12.5)
7
(29.2)
2
(8.3)
24
(100.0)
Student
9
(52.9)
4
(23.5)
3
(17.6)
1
(5.9)
0
(0.0)
17
(100.0)
Housewife
16
(38.1)
2
(4.8)
10
(23.8)
11
(26.2)
3
(7.1)
42
(100.0)
Other
6
(46.2)
4
(30.8)
0
(0.0)
1
(7.7)
2
(15.4)
13
(100.0)
Total
86
(41.7)
24
(11.7)
36
(17.5)
52
(25.2)
8
(3.9)
206
(100.0)
Less than 2
million won
19
(36.5)
7
(13.5)
11
(21.2)
14
(26.9)
1
(1.9)
52
(100.0)
Between 2~4
million won
39
(42.9)
10
(11.0)
13
(14.3)
26
(28.6)
3
(3.3)
91
(100.0)
More than 4
million won
24
(48.0)
4
(8.0)
10
(20.0)
10
(20.0)
2
(4.0)
50
(100.0)
Total
82
(42.5)
21
(10.9)
34
(17.6)
50
(25.9)
6
(3.1)
193
(100.0)
Occupation
Average
monthly
income
Sharing
Detailed
learning
Online explanation
Technical
know-how
tutor
of learning
support
and
methods
experiences
(Significance
Probability)
31.51
(.140)
3.92
(.865)
As the income increased, the demand for online tutors increased. Nevertheless, in the
fields of sharing learning know-how and experiences and technical support there was no
characteristic trend, and the response rate showed a mixed trend. As a result, there was
no statically significant difference.
(6) Requirements for active e-learning and mobile learning
In the last question of the survey, learners were asked about requirements for active
e-learning and mobile learning are. According to the data, 28.3% of the respondents
answered that it should be support for telecommunication and education expenses.
However, the reinforcement of learning motives and strategies (18.9%), the content
quality certification (18.2%), and accreditation and use (17.2%) had fairly high rates as
well.
<Table 30>Requirements for active e-learning and mobile learning
Division
Gende
r
Age
(Unit: Person, %)
E-learning
Reinforceme
Law/regulatio Telecommunicatio contents
Accredit
nt of learning Beautifu
n
n /learning co st
quality
ation
motive and l design
improvement
support
certificatio
and use
strat egies
n
Mobile
device
support
(Significa
nce
probabilit
y)
Other
s
Male
7
(6.5)
35
(32.7)
20
(18.7)
26
(24.3)
0
(0.0)
9
(8.4)
Female
8
(4.2)
49
(25.8)
34
(17.9)
30
(15.8)
1
(.5)
42
20
6
190
(22.1) (10.5) (3.2) (100.0)
9
(7.9)
28
(24.6)
16
(14.0)
21
(18.4)
0
21
13
6
114
(0.0) (18.4) (11.4) (5.3) (100.0)
3
(2.6)
35
(30.7)
28
(24.6)
18
(15.8)
1
(.9)
1
(2.2)
17
(37.8)
5
(11.1)
11
(24.4)
0
6
(0.0) (13.3)
2
3
45
(4.4) (6.7) (100.0)
2
(8.7)
4
(17.4)
4
(17.4)
6
(26.1)
0
5
(0.0) (21.7)
2
0
23
(8.7) (0.0) (100.0)
15
(5.1)
84
(28.3)
54
(18.2)
56
(18.9)
1
(.3)
26
10
297
(8.8) (3.4) (100.0)
34
years
or
younge
r
Betwee
n
35~44
Betwee
n
45~54
55
years
or older
Total
Regardless
of
the
gender,
the
respondents
19
(16.7)
51
(17.2)
most
6
4
107
(5.6) (3.7) (100.0)
9
(7.9)
1
114
(.9) (100.0)
wanted
14.55
(.042)
24.39
(.275)
support
for
telecommunication expense (men 32.6%, women 25.8%). The second most wanted
support was different with regard to gender. Men (24.3%) thought the reinforcement of
learning motives and strategies important, while women (22.1%) thought accreditation
and use important. And while 5.6% of the men opted for support for mobile devices, 10.5%
of the women chose it.
Through the data result of the e-learning and mobile learning related device
ownership, the reason for this trend can be speculated. The women appeared to own
fewer state-of-the-art devices for e-learning and mobile learning than the men, so this
difference in the e-learning and mobile learning environments may have affected the
result.
The learners wanted support for telecommunication and education expenses the most
regardless of the age. Learners between 45~54 showed the highest rate, with 37.8%.
Learners 45 years or older wanted the reinforcement of learning motives and strategies
the most, while among learners who are 55 years or older, accreditation and use also
showed a high rate. Moreover, learners between 35 and 44 asserted that e -learning
content quality certification was also needed along with support for telecommunication
and education expenses. The following table surveys the requirements for active elearning and mobile learning among learners.
<Table 31>Requirements for active e-learning and mobile learning (learners)
Division
High school
diploma
(Unit: Person, %)
Reinforcem
Law/regulat Telecommun E-learning
ent of
Beautif
Mobile
ion
ication
contents
Accredit ati
learning
ul
device Others
Improvemen /learning
quality
on and use
motive and design
support
t
cost support certification
strat egies
(Signif
icance
proba
bility)
2
(3.5)
9
(15.8)
14
(24.6)
13
(22.8)
1
(1.8)
11
(19.3)
3
4
57
(5.3) (7.0) (100.0)
7
(5.7)
44
(36.1)
19
(15.6)
18
(14.8)
0
(0.0)
16
(13.1)
15
3
122
(12.3) (2.5) (100.0)
3
(4.9)
21
(34.4)
13
(21.3)
11
(18.0)
0
(0.0)
8
(13.1)
(.151)
4
1
61
(6.6) (1.6) (100.0)
Total
12
(5.0)
74
(30.8)
46
(19.2)
42
(17.5)
1
(.4)
35
(14.6)
22
8
240
(9.2) (3.3) (100.0)
Administrativ
e
1
(6.3)
7
(43.8)
2
(12.5)
4
(25.0)
0
(0.0)
1
(6.3)
1
0
16
(6.3) (0.0) (100.0)
Professional
2
(3.8)
15
(28.8)
14
(26.9)
8
(15.4)
0
(0.0)
8
(15.4)
4
1
52
(7.7) (1.9) (100.0)
Clerical
2
(4.8)
15
(35.7)
6
(14.3)
6
(14.3)
0
(0.0)
8
(19.0)
3
2
42
(7.1) (4.8) (100.0)
1
(4.2)
7
(29.2)
4
(16.7)
6
(25.0)
0
(0.0)
1
(4.2)
4
1
24
(16.7) (4.2) (100.0)
3
(17.6)
5
(29.4)
2
(11.8)
2
(11.8)
1
(5.9)
1
(5.9)
(.379)
2
1
17
(11.8) (5.9) (100.0)
Housewife
1
(2.4)
11
(26.2)
9
(21.4)
6
(14.3)
0
(0.0)
8
(19.0)
7
0
42
(16.7) (0.0) (100.0)
Other
1
(7.7)
2
(15.4)
2
(15.4)
3
(23.1)
0
(0.0)
3
(23.1)
2
0
13
(15.4
(0.0)
(100.0)
)
Total
11
(5.3)
62
(30.1)
39
(18.9)
35
(17.0)
1
(.5)
30
(14.6)
21
7
206
(10.2) (3.4) (100.0)
3
(5.8)
17
(32.7)
6
(11.5)
7
(13.5)
1
(1.9)
10
(19.2)
6
2
52
(11.5) (3.8) (100.0) 9.22
4
(4.4)
26
(28.6)
22
(24.2)
18
(19.8)
0
(0.0)
10
(11.0)
Bachelor’s
Academic degree
qualificati Master’s
on
degree or
higher
Service·Techn
Occupatio ical
n
Student
Less than 2
Average million won
monthly
income Between 2~4
million won
19.38
44.21
(.817)
9
2
91
(9.9) (2.2) (100.0)
More than 4
million won
2
(4.0)
16
(32.0)
9
(18.0)
10
(20.0)
0
(0.0)
8
(16.0)
4
1
50
(8.0) (2.0) (100.0)
Total
9
(4.7)
59
(30.6)
37
(19.2)
35
(18.1)
1
(.5)
28
(14.5)
19
5
193
(9.8) (2.6) (100.0)
For the differences across academic qualifications, learners with bachelor’s degrees or
higher wanted support for telecommunication expenses and learning costs (36.1% and
34.4%, respectively). Whereas, the learners with only high school diplomas thought e learning contents quality certification (24.6%) and the reinforcement of learning
motives and strategies (22.8%) to be more important. Yet, the difference of .05 was not
statistically significant.
Moreover, the difference across occupation groups was not statistically significant.
The number of respondents in some occupation groups was too small to make
generalizations. And since there is a difference of one or two people in the rates, limits
to summarize the opinions of the occupation groups are strong. Looking at the responses
of the learners in professional and clerical position, and those who were housewives,
which had relatively more respondents, the learners that were professionals wanted
support for telecommunication and education expenses (28.8%), and e-learning content
quality certification (26.9%) almost equally. On the contrary, learners who were in
clerical positions wanted support for telecommunication and education expenses
(35.7%) more than support for accreditation and use (19.0%). Lastly, for housewives
there was no big difference between support for telecommunication and education
expenses (26.2%), and e-learning content quality certification (21.4%). Unlike the other
occupation groups, mobile device support was relatively higher (16.7%).
Even across the average monthly incomes, there was no statistically significant
difference. Hence, there is a limitation in generalizing results. Upon examining the
results of the categories with great differences, the learners whose income is between 2
and 4 million won showed the most demand for e-learning content quality certification,
whereas in the reinforcement of learning motives and strategies the learners whose
income is less than 2 million won showed a higher rate. Since the selection trend across
the different income groups does not appear to be directly related to the requirements
for active e-learning and mobile learning, it is difficult to determine the reason for the
difference.
After gathering the result data of the requirements, 64.8% of the respondents showed
an inclination towards lifelong learning through mobile learning. And moreover, 67.0%
of the learners showed an intention to use a smart phone application to access online
lectures. In addition, the learners’ most preferred methods of learning were listening to
online lectures via Wi-Fi (36.3%) and on MP3 players (31.6%). The need for online
tutors (41.7%) was pointed out as the most needed learning support for active e learning and mobile learning, and support for telecommunication or education expenses
was considered the most important support for active e-learning and mobile learning.
V. Conclusion and Suggestions
1. Conclusion
1) Status of participation in e-learning
Almost all (90%) of respondents have participated in e-learning, mainly via a
PC utilizing the websites of distance education institutions and cyber universities.
It has been found that the degree of e-learning participation is higher among
learners with higher degrees, and the younger the learner is, the more diverse the
mode of e-learning is. Interestingly, the degree of e-learning participation is the
highest among those above age 55, and it is the lowest among respondents aged
45-54, from which it can be inferred that people are satisfying their desire to
learn after retirement, which could not be met while they were working.
Therefore, to meet such needs, various e-learning programs targeting the elderly
should be developed. Moreover, 73.9% of the learners take e-learning programs
via PC, and 19.7% of them use laptops. Only 2.5% of the learners are using smart
phones for e-learning, which shows that the recent popular wave of smart phones
has not yet reached e-learning programs. Modes of e-learning are diverse among
young learners, and it thus can be expected that e -learning will take various
forms other PCs. Therefore, it indicates that the design of e -learning programs for
the young should be diversified according to the ir preference and life patterns.
Also among the young age group, none of the 146 women students were using
smart phones for e-learning purposes. Therefore, efforts will have to be to
increase the use of devices, including mobile e-learning, among women, in
preparation for further expansion of mobile e -learning through smart phones.
2) Awareness of and preparedness for the ubiquitous environment
Most (75.5%) of the learners use electronic networks and devices, such as the Internet,
mobile phones, and laptops. Most of the learners have various devices for e -learning
(PC,21.5%, laptops,18.8%, MP3 players,17.0%, mobile phones, 16.6%), and expressed
strong willingness to purchase devices such as smart phones, smart pads, and e-book
devices within one year (smart phones, 30.7%, smart pads, 14.0%, e-book related
devices, 14.0%). The devices they plan on purchasing within a year are those launched
recently, and already 63.6% of learners manage schedules and data through electronic
devices, which indicate that the foundation has been laid to move to a ubiquitous age
from an e-learning era.
In particular, while women have standardized e-learning related devices, men have
more diverse e-learning devices (such as iPods, Tablet PCs, smart pads, and e-book
devices). Almost none of the housewives had the latest devices, which points to the need
for guidelines and training for housewives on how to use them effectively in order to
prepare e-learning programs in the ubiquitous environment. In addition, the use of
high-end devices corresponds to the income level, and therefore, how to support costs
for e-learning devices in the ubiquitous environment should be discussed for lifelong
learning for all. Smart phones, smart pads, and e-book devices rank high among elearning devices the learners plan on purchasing within one year, and what’s interesting
is the lower the income has the stronger the willingness to purchase smart phones, and
the weakest intention to purchase smart pads. It indicates that although the use of both
devices is rapidly increasing, learners are making a reasonable choice between smart
phones and smart pads. Moreover, the lower the age group has the highest use of
electronic devices, and the highest degree of use of electronic devices is found among
those with higher educational qualifications, which indicate that the more exposed the
learners are to diverse electronic devices, the higher the use of such devices. Therefore,
more consideration should be given to lower income groups and housewives, who have
little exposure to various electronic devices.
3) Demand for e-learning for lifelong education in the ubiquitous environment
Almost two-thirds (64.8%) of respondents expressed willingness to participate in
lifelong education through mobile learning, however, the participation intention was
higher among teachers than learners: the participation score for the learners was 3.63,
for lifelong educators it was 4.10, and for teachers it was 4.2. This was because program
providers such as teachers have better access to information on the positive impact of
the ubiquitous environment, and as such, there is a need to provide opportunities for
learners so that they may share such positive expectations and design e-learning for
lifelong learning in the ubiquitous environment. When asked if they were willing to use
smart phone applications for e-learning, 67.0% of the respondents answered positively,
which indicated that they had positive expectations for a change in the e -learning modes
in the ubiquitous environment. Notably, the highest point for participation intension
(4.06) was seen among the lowest income group, which indicates that although they may
not have enough financial resources for the ubiquitous environment, they have high
expectations for learning in it. It implies that support measures for the low income
groups should be considered so that they may not be left out of the new lifelong learning
environment.
The most popular learning modes were watching Internet lecturers via Wi-Fi (36.3%)
and watching lecture footage stored in MP3 players (31.6%). In particular, while among
those below age 44, Internet lectures via Wi-Fi accounted for over 35%, among those
above age 45, lecture recordings via MP3 players accounted for 41-45%. This probably is
because the greater number of young learners have iPods, smart phones, or smart pads
which can access Wi-Fi, and they are familiar with those devices, while not many of
people above 45 have such devices, nor are they familiar with them, and as such they
prefer storing Internet lectures in MP3 players. Therefore, in designing e-learning in the
ubiquitous environment, preferences across age groups should be analyzed and
reflected. In making changes in the devices, such differences should be taken into
account, and the reason for the low preference should be identified, and measures
should be devised accordingly.
With regard to the need for mobile learning according to different areas of learning,
vocational education is ranked the highest (4.01), adult literacy is ranked considerably
low (2.84). This provides lessons on what area of learning should be focused on when
designing mobile learning programs.
Lastly, as for support for e-learning and mobile learning, online tutoring is ranked the
highest (41.7%), followed by technical support for utilization of devices and trouble
shooting (25.0%). With regard to the promotion of e-learning and mobile learning,
financial support for telecommunication/education expenses (28.3) is considered to be
the most important. It indicates that such learning support is essential for the success of
e-learning programs for lifelong education in the ubiquitous environment.
2. Suggestions
Based on the aforementioned conclusion, the following suggestions may be made for
the success of lifelong e-learning in the ubiquitous environment.
First, it is imperative to develop learning-teaching models to promote u-learning in
lifelong learning. While there are u-schools in primary and secondary education, and ucampuses in higher education, u-LLL (Lifelong Learning) does not exist at present. The
effectiveness and satisfaction with u-school and u-campus models have been proven
through extensive research and experimentation, and various value-added products
have been in use with u-learning support by the government.
The boundaries between classroom and the outside world will weaken in future
education. Various attempts will be made to promote u-learning, and they will ultimately
lead to convergence of educational systems. Therefore, there is a need for a medium to
long term change in the relationship between u-School/u-Campus and u-LLL (Lifelong
Learning). Lifelong learning has a positive impact on many factors of social cohesion,
such as modes of formal education for adults (Su-Myung Jang, 2009). Moreover, until the
early 20th century, knowledge learned during school years was utilized for life . Now,
however, quantity of knowledge changes so swiftly that a junior student of engineering
will have to update the knowledge gained in the freshman year by up to 50% before
graduating. Therefore, with endless learning, adequate learning-teaching models must
be developed for effective lifelong learning.
Second, there should be support for those social groups that are left out of the
ubiquitous learning environment. U-learning infrastructure support is needed to
promote u-learning and to bridge the gap in u-learning information. Such examples
include provision of u-learning devices for the underprivileged in terms of ICT, financial
support for telecommunication and education expenses. According to the information
gap index by the Ministry of Public Administration Security in 2010, the information
index of the underprivileged class as compared to the national average was 69.7%.
However, the budget allocation for improving the information index of the
underprivileged declined from 25.3 billion in 2007 to 18.4 billion in 2009 (2009.
National Information Society Agency). The rate of Internet use of the underprivileged as
compared to the national average was 77.6%, the rate of PC distribution was 81.4%, and
examination by sub-category revealed that the rate for the disabled as compared to the
national average was 80.3%, 79.5% for lower income groups, 65.9% for the elderly, and
60.3% for farmers/fishermen who were ranked the lowest (2010 Information gap index
survey). To improve the situation, the Ministry of Education, Science and Technology
has instituted support measures for children of lower income groups, providing PCs, and
financial support for Internet bills: Between 2000 and 2009 about 920,000 people
(2000-2009) received support. However, there was no support for adult or out-ofclassroom learners. Therefore, there is a need for support measures, such as vouchers
for u-learning, to increase the accessibility of the ICT underprivileged to u -learning and
reduce social conflicts.
Third, there is a need for educational support not only to ensure u-learning
accessibility, but also to enhance learners’ self-initiative. In today’s ubiquitous
environment, such traits as independent discipline, self-monitoring, self-motivation and
self-learning are of particular importance. “Learning how to learn,” a state or skill of
being cognizant of one’s own learning, and independent learning, have long been the
primary objectives of adult education (Caffarella 1993:29) As Caffarella (1993) observed
that self-initiative is the ability to decide content, place, methods, and pace of learning,
and it is a very significant skill to actively cope with personal and social changes. To
foster this ability, the teacher will have to regard his/her role as guide and facilitator
rather than deliverer, and will have to provide learners with opportunities to make
decisions for their learning, and through appropriate teaching-learning techniques, help
learners to manage their learning process in a responsible manner.
Fourth, there should be educational assessment of technologies, not just about their
use as a tool. To this end, the origins of various technologies, and their characteristics
and functions must be understood. Moreover, based on such understanding, the kind of
impact technologies have with regard to the formal/informal/non-formal learning of
various learners groups must be explained. For example, there should be technical
research on not only educational requirements of learners’ groups in the ubiquitous era,
but also on the characteristics of hypertext and learning impact, awareness of a specific
technology among diverse learners’ groups, and its modes of use, and various forms of
learning in the ubiquitous environment. Such research will form useful basic data to
build an educational environment that can foster lifelong learning and educational
growth of learners of diverse age groups, social classes, gender, and different racial
backgrounds in the ubiquitous society.
VI. References
강이철 외 3 인(2007). (이슈리포트) u-스쿨: 그 가능성과 한계. 한국교육학술정보원.
곽덕훈 (2009) 교육과학기술부 간부 정보화전략 연수자료.
교육과학기술부 (2009). 평생교육백서 2009, 평생교육진흥원
권성호 외 3 인(2006). u-러닝 코리아 로드맵 연구. 한국교육학술정보원.
권성호 외 7 인(2006). u-러닝 효과성 분석 연구. 한국교육학술정보원.
김재윤, 권기덕, 임진호(2004). 유비쿼터스 컴퓨팅 환경에서의 교육의 미래 모습.
서울:한국교육학술정보원.
김재윤, 권기덕, 임진화 (2004) 「유비쿼터스 컴퓨팅환경에서의 교육의 미래 모습」,
한국교육학술정보원 연구보고서 KR 2004-27
박근상, 김영훈, 강성욱, 김창한 (2007). 교육정보화 사업의 성공 지수 도출을 위한 평가지표
개발. 한국교육정보미디어연구, 13, p. 221-247
박영숙, 제롬 글렌, 테드 고든(2008). 유엔미래보고서: 미리 가본 2018 년. 교보문고.
백광현(2009).
이러닝에서
유러닝으로의
연계방안
연구.
한국경영교육학회
추계학술대회 논문집
손미 (2007). 유비쿼터스 학습 환경에서 체험학습의 가능성과 실현 조건 탐색.
교과교육학연구, 11(1), 143-172.
윤상오(2008). 유비쿼터스 미래정부 구현을 위한 정부신뢰 구축에 관한 연구. 한국정책학회.
2008(4). pp705-734.
이동만, 이상희 (2009). U-learning 시스템의 유비쿼터스 속성이 학습자의 상호작용성 및
학습성과에 미치는 영향에 관한 연구. 한국산업교육학회 경영연구, 24(1), pp 33-68
이수경 외 2 인(2007). 유비쿼터스 환경에 대응한 직업능력개발 지원방안 연구.
한국직업능력개발원
이재분 외 3 인(2006). 유비쿼터스 사회에서의 평생학습 활성화 정책방안 연구.
한국교육개발원.
이혜영 외 4 인(2008). 미래 학교 모형 탐색 연구. 한국교육개발원.
장수명(2009). 평생학습과 사회통합. 평생학습의 사회․경제적 성과 분석과 전망, 제 33 차
KRIVET 인재개발(HRD) 정책포럼. 한국직업능력개발원.
Alheit, P., & Dausien, B. (2002). The double face of lifelong learning: T 재 analytical
perspectives on a silent revolution. Studies in the Education of Adults, 34(1), 3-22
Dodds, T. (2003). Universities, adult basic education, open and lifelong learning and new
technology. Retrieved August 12, 2006, from
http://www.eldis.org/static/DOG13384.htm
Hylén (2008). Open Educational Resources: Opportunities and Challenges. OECD's
Center for Educational Research and Innovations. Background Report.
Jung, W. S., Choi, K. A., & Baik. E. S. (2010). e-Learning for lifelong learning in South Korea, In
e-ASEM White Paper: e-Learning for lifelong learning. KNOU Press, pp. 357-452.
Karl
Fisch
(2010).
Did
You
Know?/Shift
Happens
3.0.
http://thefischbowl.blogspot.com/2008/08/did-you-know-music-industryremix.html
Kerka, S. (2000). Extending Information Literacy in Electronic Environments. New
Directions for Adult and Continuing Education, 2000(88). pp. 27- 38.
Lim, So & Tan (2010). eLearning 2.0 and New Literacies: Are Social Practices Lagging
behind? Interactive Learning Environments, 18-3. pp.203-218.
OECD (2001). Schooling for Tomorrow: what Schools for the Future?
OECD (2007). Giving Knowledge for Free. The emergence of Open Educational
Resources.
Vavoula, G., & Sharples, M. (2009). Lifelong Learning Organisers: Requirements for tools
for supporting episodic and semantic learning. Educational Technology & Society,
12(3), 82-97.
Yamamoto, D., Takumi, I., Matsuo, H.(2009). A Location-Based Social Network Service
based on Authentication Records of Smart Cards, DBSJ Journal, 8(3),pp. 13-18.